M u s c u l o s k e l e t a l I m a g i n g • R ev i ew Sanchis-Sánchez et al. Infrared Thermal Imaging of Musculoskeletal Injuries
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Musculoskeletal Imaging Review
Infrared Thermal Imaging in the Diagnosis of Musculoskeletal Injuries: A Systematic Review and Meta-Analysis Enrique Sanchis-Sánchez1 Carlos Vergara-Hernández 2 Rosa M. Cibrián 3 Rosario Salvador 3 Enrique Sanchis 2 Pilar Codoñer-Franch 4 Sanchis-Sánchez E, Vergara-Hernández C, Cibrián RM, Salvador R, Sanchis E, Codoñer-Franch P
Keywords: diagnostic imaging, meta-analysis, musculoskeletal injuries, sensitivity and specificity, stress fractures, systematic review, thermography DOI:10.2214/AJR.13.11716 Received August 1, 2013; accepted after revision October 4, 2013. 1 Department of Physical Therapy, University of Valencia, C. Gasco Oliag 5, Valencia E-46010, Spain. Address correspondence to E. Sanchis-Sánchez ([email protected]). 2 Department of Electronic Engineering, University of Valencia, Valencia, Spain. 3
Department of Physiology, University of Valencia, Valencia, Spain. 4 Department of Pediatrics, Obstetrics, and Gynecology, University Hospital “Dr. Peset,” University of Valencia, Valencia, Spain.
AJR 2014; 203:875–882 0361–803X/14/2034–875 © American Roentgen Ray Society
OBJECTIVE. Musculoskeletal injuries occur frequently. Diagnostic tests using ionizing radiation can lead to problems for patients, and infrared thermal imaging could be useful when diagnosing these injuries. CONCLUSION. A systematic review was performed to determine the diagnostic accuracy of infrared thermal imaging in patients with musculoskeletal injuries. A meta-analysis of three studies evaluating stress fractures was performed and found a lack of support for the usefulness of infrared thermal imaging in musculoskeletal injuries diagnosis.
A
ccording to data collected during the year 2010, in the United States, 14,386,192 people had to attend hospital emergency departments as a result of musculoskeletal injuries, including fractures, sprains, dislocations, contusions, and compartment syndrome (International Classification of Diseases, ninth revision, Clinical Modification diagnosis codes 810–848, 920–929, and 958.9). Of those patients, 610,946 were discharged from hospital emergency departments to short-term hospitals, nursing homes, rehabilitation centers, and home health care [1], making this type of injury a frequent reason for seeking medical attention. Diagnostic imaging procedures are essential in the diagnosis of musculoskeletal injuries. These diagnostic studies usually consist of unenhanced or contrast-enhanced radiography, CT, MRI, or ultrasound. It is the clinic’s job to make the final decision as to whether these studies are appropriate in the management of each patient [2]. Moreover, the increase in the frequency of use of tests that emit ionizing radiation (e.g., in the United States, the per capita annual effective dose resulting from medical procedures increased sixfold between 1980 and 2006 [3]) could overexpose the general population [4, 5], but especially infants and unborn children [6], to unnecessary radiation doses, contradicting the “As Low As Reasonably Achievable” principle, which is based on the justification, optimization, and limitation of the dose. Infrared thermal imaging is a technique whose application in the field of health sci-
ences allows an image to be taken of the infrared radiation produced naturally by the skin, which acts like an almost perfect black body [7]. In this way, the technique can detect local variations in temperature, such as those that occur in inflammatory conditions associated with different pathologic abnormalities, including musculoskeletal injuries. Hence, it could act as a diagnostic complement by adding a physiologic profile study to the anatomic one [8] offered by traditional imaging studies, which could reduce the need for the latter, thus reducing population exposure to ionizing radiation. However, although there are reviews concerning the application of infrared thermal imaging as a diagnostic tool in health sciences [9–12], up to now we are not aware of any systematic review investigating its usefulness in the diagnosis of musculoskeletal injuries; therefore, we decided to perform this systematic review with the aim of determining the diagnostic accuracy of infrared thermal imaging for said lesions. Materials and Methods A systematic review of the diagnostic test accuracy was proposed, according to the guidelines set out by the Cochrane Collaboration [13].
Data Sources and Searches A series of electronic searches were performed using the following databases: MEDLINE (via PubMed), EMBASE (Elsevier), CINAHL (EBSCO), Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effect
AJR:203, October 2014 875
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Sanchis-Sánchez et al. (DARE), Cochrane Central Register of Controlled Trials (CENTRAL), SCOPUS, Web of Science, IME, and ICYT. No time limit was set, and everything covered in each database up to January 8, 2013, was included. The search strategy for each database was outlined around three conceptual areas (infrared thermal imaging, musculoskeletal injury, and diagnostic test accuracy), widening each of these areas as much as possible by incorporating synonyms, resulting in a set of terms in the form of a free text and terminology from the thesaurus of each database when possible. Later, the set intersection was determined, this being the end result of the entry searches. Table 1 shows the search strategy used for the MEDLINE (via PubMed) database. During the document selection process, the reference lists of the studies were reviewed as the studies were included in the review, as well as the reviews of the literature or clinical practice guides found.
TABLE 1: Search Strategy Followed in the MEDLINE (via PubMed) Database Strategy Number
Description
1
Thermography[MeSH Terms] OR “thermograph*”[Title/Abstract] OR “thermal image”[Title/ Abstract] OR “thermal imaging”[Title/Abstract] OR “infrared photography”[Title/Abstract] OR “diti”[Title/Abstract] OR “infrared image”[Title/Abstract] OR “infrared imaging”[Title/ Abstract]
2
(Wounds and injuries[MeSH Terms]) OR injuries[MeSH Subheading] OR injur*[Title/Abstract] OR trauma*[Title/Abstract] OR sprain*[Title/Abstract] OR strain*[Title/Abstract] OR fracture*[Title/Abstract] OR (“bone broke”[Title/Abstract]) OR dislocation*[Title/Abstract] OR tendinopathy[Title/Abstract] OR luxation*[Title/Abstract] OR contusion*[Title/Abstract] OR tendon*[Title/Abstract] OR (“muscle tear”[Title/Abstract])
3
diagnosis[MeSH Subheading] OR diagnos*[Text Word]
4
#1 AND #2 AND #3
lection criteria as used in the first phase were independently applied, and any conflicts between the two reviewers were resolved by the intervention of a third reviewer.
Data Extraction and Quality Assessment Study Selection Studies were included that met the following criteria: they evaluated diagnostic accuracy, offering sufficient information to allow 2 × 2 tables to be calculated (true-positives [TPs], true-negatives [TNs], false-positives [FPs], and false-negatives [FNs]), without setting development time limitations (transversal vs longitudinal) or monitoring limitations (retrospective vs prospective); they described one or several samples dealing with living human subjects, without setting any other restrictions; they made use of infrared thermal imaging (using any type of apparatus or means) in the diagnosis of musculoskeletal injuries (e.g., bone fractures, dislocations, sprains, muscle contractures, tendinopathy, contusions, or compartment syndrome); they used as a reference standard the clinical interpretation of image diagnostic exploratory tests (e.g., unenhanced or contrast-enhanced radiographs, CT, MRI, or ultrasound scanning); and they were written in English, French, Spanish, Italian, or Portuguese. The results of the searches were transferred into an electronic reference manager (RefWorks 2.0, ProQuest), eliminating duplicated entries. After this, two reviewers independently read the titles, abstracts, and keywords of the entries to determine their pertinence and relevance on the basis of the predefined criteria and to decide whether they should be included in the review. Any conflicts that arose were resolved by the intervention of a third reviewer. In the next phase, two reviewers read the complete texts of the entries included in the first phase. Because the analysis unit was the study, it was decided to reject the entries that referred to a study that had previously been included. The same se-
876
Two reviewers independently extracted the data using a template containing the identification of the study, selection criteria of the study, methodologic quality assessment, main characteristics of the study (e.g., participants, age, sex, excluded participants, losses during the study, and sampling technique), a description of the diagnostic tests and their interpretation (index test, comparator tests, and reference tests), a description of the measurements of the result, and the main results of the study (e.g., 2 × 2 table, sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio [OR], and the presence of a receiver operating characteristic [ROC] curve and, if so, the area under the curve). Information was also collected concerning the prevalence of the result, changes in the technology used, the previous concise setting of the values for a positive test, the training of the people who interpreted the diagnostic tests, the use of a controlled measurement range, and whether the subjects received treatment during the interim period between the two tests. Conflicts over extraction of data and the analysis of the methodologic quality were resolved by the intervention of a third reviewer. Methodologic quality assessment was performed according to the recommendations of the Cochrane Collaboration for systematic reviews of diagnostic test accuracy studies [14], using a template developed with the 11 items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool [15]. This was performed independently by two reviewers, with disagreements resolved by the intervention of a third reviewer. To show the results, a summary table for each of the included studies was developed, along with a figure for the set of studies included in the review.
Data Synthesis and Analysis In both document selection phases, the interrater reliability was calculated by means of the kappa index (95% CI computed with 2000 bootstrap samples [adjusted bootstrap percentile]). The necessary information to build 2 × 2 tables for each study was collected, and the data to calculate estimations of sensitivity (TP / [TP + FN]) and specificity (TN / [TN + FP]) (with 95% CI) from the index test were extracted. These were then represented on a forest plot. We decided to perform a meta-analysis for each result and to estimate a combined value only when the absence of high heterogeneity and inconsistency between the studies included could be corroborated. Heterogeneity was evaluated with the chisquare test, and inconsistency was evaluated with the Higgins I-square test. We decided not to provide a combined presentation of the results when the heterogeneity showed statistically significant results and when the inconsistency was over 75%. For the presentation of the results, we decided to choose between a point estimate of the pooled values in the meta-analysis (by means of a forest plot) or a presentation in the ROC space using a summary ROC curve [16]. The decision to use one of them was based on finding variability in the diagnostic threshold, which was assessed by means of the Spearman correlation coefficient between the logit of sensitivity and the logit of 1 − specificity. We decided that when a statistically significant variability in the diagnostic threshold was identified, the results were pooled by means of a summary ROC curve. To identify possible sources of heterogeneity and to perform a stratified meta-analysis that excluded these sources (as long as the number of studies was sufficiently high [17]), a meta-regression by means of a generalization of Littenberg and Moses linear model was planned. All the statistical analyses were performed with a significance level of α = 0.05, using MetaDiSc software (version 1.4) [18] and R software
AJR:203, October 2014
Infrared Thermal Imaging of Musculoskeletal Injuries
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
(version 3.0.0; packages mada, psy, and boot; The R Foundation for Statistical Computing).
Fig. 1—Flow diagram of studies considered for review.
1727 Records identified through electronic database searching
Results Figure 1 shows the flow diagram of the studies considered in the review. After we performed electronic bibliographic searches in the databases, a total of 1727 search results were found, and no results from any other data sources were found. After eliminating 318 duplicated search results, 1409 results were subjected to the first narrowing down process, and 1393 results were eliminated after we read their title, abstract, or keywords because of a lack of relevance with regard to the established selection criteria. Among the 16 articles that were read through fully to consider their eligibility, none of them was found to make reference to a single study; hence, they were taken to be independent studies. Ten of these studies were finally excluded: eight because they did not conform to a methodologic design to evaluate diagnostic accuracy [9, 19–25], one because it did not use an appropriate reference standard [26], and the last one because it was a
0 Records identified through other sources
1409 Records after duplicates removed
Not relevant: 1393 Records excluded
1409 Records screened for title, abstract, and keywords
10 Articles excluded: • Study design: 8 • Index test: 0 • Reference standard: 1 • Outcome: 0 • Population: 1
16 Full-text articles accessed for eligibility
6 Studies included in qualitative synthesis
3 Studies excluded from the quantitative synthesis (meta-analysis): • Unable to extract data: 2 • Wide outcome: 1
3 Studies included in quantitative synthesis (meta-analysis)
TABLE 2: Characteristics of the Included Studies Dalinka et al. [30]
Characteristic Number of subjects
83
Meurman et al. [31] 84
Devereaux et al. [32] 18
Goodman et al. [33] 17
Hosie et al. [28] 50
Katz et al. [29] 164
Design
Prospective
Prospective
Prospective
Prospective
Prospective
Prospective
Setting
Not stated
Military recruits
Athletes
Athletes
Emergency department patients
Emergency department patients
Reason for presentation
Suspected problem Suspected stress fracture
Suspected stress fracture
Suspected stress fracture
Scaphoid fracture diagnosed
Suspected compartment syndrome
Mean age of patients (y)
Not stated
20.6
23
Not stated
Not stated
36
Male patients (%)
Not stated
100
61
59
Not stated
Not stated
Outcome prevalence (%)
69
76
83
65
100
7
Camera
Tektronix
Aga-Thermovision 680 Medical
Not stated
Dorex
Not stated
L-Wave infrared camera
Camera calibration
Not stated
Not stated
Not stated
Not stated
Not stated
Yes
Image quality assessment
Not stated
Not stated
Not stated
Not stated
Not stated
Yes
Image analysis
Visual: temperature Visual: temperature Not stated asymmetry asymmetry
Computerized: area Visual: temperature Computerized: and temperature asymmetry temperature asymmetry asymmetry
Interrater reliability (intraclass Not stated correlation coefficient)
Not stated
Not stated
Not stated
Not stated
0.98
Acclimation time (min)
10
15
10
Not stated
Not stated
10
Region of interest selection
Not stated
Yes
Yes
Yes
Yes
Yes
Distance (m)
Not stated
Not stated
1
Not stated
Not applicable
0.9
Researchers training
Not stated
Not stated
Not stated
Not stated
Not stated
Yes
Environment temperature (°C)
20
Not stated
20
Not described
Not stated
Not stated
Relative humidity (%)
Not stated
Not stated
Not stated
Not stated
Not stated
Not stated
AJR:203, October 2014 877
Sanchis-Sánchez et al. TABLE 3: Methodologic Quality Summary of the Included Studies According to the Items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) Tool
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
QUADAS Item
Dalinka et al. [31]
Meurman et al. [31] Devereaux et al. [32] Goodman et al. [33]
Hosie et al. [28]
Katz et al. [29]
Withdrawals
No, low quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Noninterpretable results
No, low quality
Unclear quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Clinical review
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Diagnostic review
Yes, high quality
Unclear quality
Unclear quality
Unclear quality
Unclear quality
Yes, high quality
Test review
Yes, high quality
Unclear quality
Unclear quality
Unclear quality
Unclear quality
Yes, high quality
Incorporation
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Differential verification
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Partial verification
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Disease progression
Unclear quality
Unclear quality
Unclear quality
Unclear quality
No, low quality
Yes, high quality
Reference standard
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Patients’ spectrum
Yes, high quality
Yes, high quality
Yes, high quality
No, low quality
Unclear quality
Yes, high quality
study using corpses [27]. The interrater reliability for the first narrowing down study selection process was κ = 0.702 (p < 0.01; 95% CI, 0.476–0.848), whereas for the second one it was κ = 0.871 (p < 0.01; 95% CI, 0.4–1). Characteristics of the six studies included in the review are shown in Table 2. In two of them [28, 29], it was not possible to extract the necessary information to build a 2 × 2 table despite the fact that the authors reported information about the diagnostic accuracy of infrared thermal imaging on a total of 214 patients (these studies investigated scaphoid bone fractures and compartment syndrome, respectively). Despite efforts made to contact the authors of these studies, no replies were received; therefore, they could not be included in the quantitative synthesis. An analysis of the methodologic quality of the six studies included (Table 3) found a low bias risk (i.e., a low risk for all the items on the QUADAS tool) for the study by Katz
et al. [29] and a moderate-to-high risk for the remaining studies. In a detailed analysis, four studies used consecutive sampling on a range of patients [29–32], one did not describe the sampling technique used [28], and another described convenience sampling [33]. All the studies used an appropriate reference standard, which varies from the interpretation of unenhanced and contrastenhanced radiographic techniques, to the combination of these techniques with a meticulous physical examination. With regard to the time period between the application of the reference standard test and the index test, only one study [29] provided adequate information, four studies were not very clear on this point [30–33], and one gave no information whatsoever [28]. The use of blinding techniques between the results of the index test and the reference standard are not clear in four of the six studies [28, 31–33]. One study did not adequately clarify wheth-
er there were noninterpretable results [31], whereas another [30] showed a clear disparity in the form of noninterpretable results and unexplained losses. Hosie et al. [28] studied infrared thermal imaging in the diagnosis of scaphoid bone fractures, reporting a sensitivity of 0.77 (with three FN results), a specificity of 0.82 (with seven FP results), and a negative predictive value of 0.9 (outcome prevalence, 100%). Because there was no 2 × 2 table, an attempt was made to calculate the TP and TN rates, but the values obtained did not match those given by the authors. Katz et al. [29] studied infrared thermal imaging in the diagnosis of compartment syndrome, and they reported the presence of an ROC curve and presented an estimated area under the curve value that was equal to 1 when comparing the temperature of the thigh with that of the foot and equal to 0.98 when only taking into account the temperature of
TABLE 4: Diagnostic Performance of Infrared Thermal Imaging in the Diagnosis of Stress Fractures
Study
No. of True False False True Sensitivity (CI Patients Positive Positive Negative Negative 95%)
Specificity (CI 95%)
Positive Negative Likelihood Ratio Likelihood Ratio Diagnostic Odds (CI 95%) (CI 95%) Ratio (CI 95%)
Meurman et al. [31]
84
29
8
35
12
0.453 (0.328–0.583)
0.6 (0.361–0.809)
1.13 (0.62–2.06)
0.911 (0.598–1.389)
1.243 (0.448–3.45)
Devereaux et al. [32]
18
12
0
3
3
0.8 (0.519–0.957)
1 (0.292–1)
6.25 (0.46–84.63)
0.25 (0.092–0.678)a
25 (1.028–608.10)a
Goodman et al. [33]
17
9
1
2
5
0.82 (0.482–0.977)
0.833 (0.359–0.996)
4.91 (0.8–30.02)
0.218 (0.059–0.804)a
22.5 (1.609–314.57)a
10.05 (p < 0.01)
3.6 (p = 0.17)
4.22 (p = 0.12)
8.73 (p = 0.01)
6.48 (p = 0.04)
80.1
44.4
52.5
77.1
69.1
Heterogeneityb Inconsistency (%)c
Note—We added 0.5 to all cells of the studies with zero. Stress fracture prevalence was 76% in Meurman et al. [31], 83% in Devereaux et al. [32], and 65% in Goodman et al [33]. aThe value of 1 is not between the values of the 95% CI. bAssessed through the chi-square test (degrees of freedom, 2). cAssessed through the Higgins I-square test.
878
AJR:203, October 2014
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Infrared Thermal Imaging of Musculoskeletal Injuries the foot (outcome prevalence, 7%). However, no information was given about either the 95% CI or the FP, FN, and TN rates, and these could not be calculated by the reviewers because they did not have the raw research data. Dalinka et al. [30] studied a sample of 83 research subjects (outcome prevalence, 60%), reporting a sensitivity of 0.74 (95% CI, 0.61–0.85) and a specificity of 0.44 (95% CI, 0.24–0.65). However, the result assessed was too broad because it grouped together diverse knee pathologic abnormalities without differentiating among them in the analysis phase; therefore, it could not be included in the quantitative synthesis. Meurman et al. [31], Devereaux et al. [32], and Goodman et al. [33] investigated the same pathologic abnormalities (stress fractures), including a total of 119 research subjects, 90 of whom developed stress fractures. These three studies were included in the quantitative synthesis. Table 4 shows the diagnostic performance values of infrared thermal imaging. With regard to heterogeneity, the chisquare test was statistically significant for sensitivity (χ2 = 10.05; degrees of freedom [df] = 2; p < 0.01), negative likelihood ratio (χ2 = 8.73; df = 2; p = 0.01), and diagnostic OR (χ2 = 6.48; df = 2; p = 0.04), but was not significant for specificity (χ2 = 3.60; df = 2; p = 0.17) and positive likelihood ratio (χ2 = 4.21; df = 2; p = 0.12). High inconsistency was found for sensitivity (Higgins I2 = 80.1%), negative likelihood ratio (Higgins I2 = 77.1%), and diagnostic OR (Higgins I2 = 69%), and moderate inconsistency was found for specificity (Higgins I2 = 44.4%) and positive likelihood ratio (Higgins I2 = 52.5%). It was considered inappropriate to perform a meta-regression to explore the sources of heterogeneity because of the low number of studies found. Because of all these factors, we decided to present a combined result only for specificity and positive likelihood ratio. The Spearman correlation coefficient between the logit of sensitivity and the logit of 1 − specificity was not significant (ρ = −0.5; p = 0.67), so there is no conclusive proof of the existence of variability in the diagnostic threshold between the studies; thus, we decided to pool results only by means of a forest plot. Figure 2 shows the forest plots for the sensitivity, specificity, and likelihood ratios of the studies that were included in the quantitative analysis. In Figure 2, you can see the high variability for sensitivity and negative likelihood ratio among the studies of Meurman
Sensitivity (95% CI) Meurman et al. [31] Devereaux et al. [32] Goodman et al. [33]
0
0.2
0.4 0.6 Sensitivity
0.45 (0.33 − 0.58) 0.80 (0.52 − 0.96) 0.82 (0.48 − 0.98)
Chi-square = 10.05; df = 2 (p < 0.01) 1 Inconsistency (I-square) = 80.1%
0.8
A Specificity (95% CI) Meurman et al. [31] Devereaux et al. [32] Goodman et al. [33]
0
0.2
0.4 0.6 Specificity
0.60 (0.36 − 0.81) 1.00 (0.29 − 1.00) 0.83 (0.36 − 1.00)
Pooled Specificity = 0.69 (0.49 to 0.85) Chi-square = 3.60; df = 2 (p = 0.1656) 1 Inconsistency (I-square) = 44.4%
0.8
B Positive Likelihood Ratio (95% CI) Meurman et al. [31] 1.13 (0.62 − 2.07) Devereaux et al. [32] 6.25 (0.46 − 84.63) Goodman et al. [33] 4.91 (0.80 − 30.02)
0.01
1 Positive Likelihood Ratio
Random Effects Model Pooled Positive Likelihood Ratio = 2.31 (0.63 to 8.47) Chi-square = 4.21; df = 2 (p = 0.1216) 100.0 Inconsistency (I-square) = 52.5% Tau-squared = 0.7156
C Negative Likelihood Ratio (95% CI) Meurman et al. [31] Devereaux et al. [32] Goodman et al. [33]
0.01
1 Negative Likelihood Ratio
0.91 (0.60 − 1.39) 0.25 (0.09 − 0.68) 0.22 (0.06 − 0.80)
Chi-square = 8.73; df = 2 (p = 0.01) 100.0 Inconsistency (I-square) = 77.1% Tau-squared = 0.6619
D Fig. 2—Forest plots of data from diagnostic test accuracy studies of infrared thermal imaging in diagnosis of stress fractures. A–D, Graphs show sensitivities (A), specificities (B), positive likelihood ratio (C), and negative likelihood ratio (D).
et al. [31] (0.45 [95% CI, 0.33–5.58] and 0.91 [95% CI, 0.6–1.39], respectively), Devereaux et al. [32] (0.80 [95% CI, 0.52–0.96] and 0.25 [95% CI, 0.09–0.68], respectively), and Goodman et al. [33] (0.82 [95% CI, 0.48–0.98] and 0.22 [95% CI, 0.06–0.80], respectively). Although the values for specificity and positive likelihood ratio were 0.60
(95% CI, 0.36–0.81) and 1.13 (95% CI, 0.62– 2.07) , respectively, in the study by Meurman et al. [31], 0.83 (95% CI, 0.36–1.00) and 4.91 (95% CI, 0.80–30.02), respectively, in Goodman et al. [33], and 1.00 (95% CI, 0.29–1.00) and 6.25 (95% CI, 0.46–84.63), respectively, in Devereaux et al. [32], the combined estimate of specificity was 0.69 (95% CI, 0.49–
AJR:203, October 2014 879
Sanchis-Sánchez et al.
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
0.85) and the positive likelihood ratio was 2.31 (95% CI, 0.63–8.47), thus encompassing the 1 value between the 95% CIs. Discussion Despite the methodologic limitations found in the included studies, our results show that infrared thermal imaging has a moderate diagnostic accuracy for stress fractures. Although there are promising results regarding the role of infrared thermal imaging in the diagnosis of compartmental syndrome, nothing conclusive can be inferred about the role of infrared thermal imaging for the remaining musculoskeletal injuries. The results of a recent systematic review concerning the diagnosis of stress fractures by means of ultrasound and tuning forks [34] shows results similar to those offered by infrared thermal imaging in this review (pooled results for ultrasounds: sensitivity, 0.642 [95% CI, 0.548–0.727]; specificity, 0.631 [95% CI, 0.542–0.712]; positive likelihood ratio, 2.092 [95% CI, 1.099– 3.517]; negative likelihood ratio, 0.35 [95% CI, 0.079–0.861]; and diagnostic OR, 6.199 [95% CI, 0.697–22.75]), with the advantage that infrared thermal imaging does not produce pain for the patient, in contrast with the aforementioned procedures. However, the results pooled in the meta-analysis (performed only for ultrasounds) undertaken by Schneiders et al. [34] may not be consistent because of the absence of heterogeneity assessment, especially when there seems to be a certain discrepancy between the results provided in the forest plots for sensitivity, specificity, negative likelihood ratio, and diagnostic OR. The low number of studies found, the methodologic differences, and the unexplained heterogeneity found in the different studies mean that the results obtained have to be viewed with caution. These differences could have arisen because of diverse factors that mainly pivot around an inadequate design of a diagnostic accuracy study [35], such as a sample with an excessively high results prevalence, the lack of blind testing in the results for both the index test and the reference standard, lack of a description of the measuring protocol, and the analysis, especially the way in which the collected information was adjusted as much as possible to reality, whether by controlling the variables of the surroundings or by using a visual rather than computerized analysis (Table 2). All these factors can lead to a bi-
880
ased estimate of the diagnostic accuracy of infrared thermal imaging [36]. With regard to the features of infrared cameras, the age of the equipment used, the fact that the studies were quite old, and the type of data analysis all could very well explain the low values of diagnostic accuracy. It is noteworthy that several of the studies included in the review do not describe the cameras used [28, 32]. They do not mention their technical characteristics or calibration with regard to a black body, nor do they evaluate their image quality [28, 30–33]. This limited information concerning the cameras could lead to a biased interpretation of the results. Although there are guidelines that contain recommendations for performing a study that uses infrared thermal imaging as a diagnostic tool [37–41] (e.g., some factors to be considered when performing infrared thermal imaging are medication, waiting time, atmospheric temperature, cutaneous products, history of vasomotor problems, sensitivity of the camera, relative humidity, direct ventilation, existence of a measurement protocol, measurement angle, distance, training, conduct, and general state of the subjects), to our knowledge, no study directly refers to these factors. In the studies we reviewed, only some of the factors that are described in the guidelines were controlled; therefore, the measurements could be yielding biased values. Limitations of the Review The synthesis of the quantitative results could have been limited by combining injuries that occurred in different parts of the body. However, there is research suggesting that the acceptance of equality of temperature between opposing sides of the body is valid [42, 43], which minimizes the possibility of variation due to this combination. Because the reviewers did not always agree with regard to the selection of the articles and studies for inclusion in the review, this may lead one to think that the criteria for selection and exclusion were not correctly followed. However, the high values given by the kappa index show that there was a high rate of agreement, reducing this possibility. The establishment of language limits as selection criteria could have led to a potential selection bias, but taking into account the combined coverage of the various databases consulted, we think that this has been minimized to a great extent.
Despite the fact that the Cochrane Collaboration [44] and other authors [45] do not recommend the systematic use of methodologic filters as part of the search strategy for diagnostic test accuracy systematic reviews, we decided to use a generic filter (third conceptual combination in the search strategy) as a consequence of the wide range of results that proved to be of interest with regard to the aims of the review. Implications for Practice Regarding the role of infrared thermal imaging as a diagnostic tool for musculoskeletal injuries, the few studies found (this meant that the Spearman correlation coefficient between the logit of sensitivity and the logit of 1 − specificity was not statistically significant because of a lack of power); the high heterogeneity and inconsistency for sensitivity, negative likelihood ratio, and diagnostic OR, which ruled out their combination; the low combined specificity value (0.69 [95% CI, 0.49–0.85]) and positive likelihood ratio (2.31 [95% CI, 0.63–8.47]); and the low methodologic quality mean that there is no convincing evidence of its clinical utility. Thus, for the time being, infrared thermal imaging should not be incorporated into clinical routines. Implications for Future Research Since the end of the 1990s, infrared thermal imaging technology has evolved, becoming much more accurate and reliable [7, 46]; this, along with the results offered regarding its use in other types of injuries [47], should lead to the prospect of future research. In addition, the inherent values of infrared thermal imaging itself (it is a fast, direct, noninvasive, not overcomplicated, and relatively moderately priced method) would suggest an increase in the possibilities and usefulness of this technique. This is something that should be investigated in the future because of its potential implications for the improvement of health care. An area where infrared thermal imaging could be extremely useful would be in the diagnosis of injuries in populations who are especially vulnerable to ionizing radiation, such as children, for whom it has already been studied not only for musculoskeletal injuries [24] but also for other pathologic abnormalities [48], or pregnant women. However, future research must adequately address the most problematic aspects
AJR:203, October 2014
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Infrared Thermal Imaging of Musculoskeletal Injuries that have been identified in this review to clarify the usefulness of infrared thermal imaging as a diagnostic tool (selection of research subjects, prevalence of result values, or the technical characteristics, calibration, and protocol for the acquisition of images with the equipment). To do this, it will be necessary to systematically follow the STARD Statement checklist [49], as well as the specific guidelines concerning infrared thermal imaging [37–41]. References 1. Healthcare Cost and Utilization Project (HCUP). Overview of the state emergency department databases (SEDD). HCUP website. www.hcup-us. ahrq.gov/seddoverview.jsp. Published 2010. Accessed February 25, 2014 2. Sistrom CL. The appropriateness of imaging: a comprehensive conceptual framework. Radiology 2009; 251:637–649 3. Mettler FA, Bhargavan M, Faulkner K, et al. Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources—1950–2007. Radiology 2009; 253:520–531 4. Brenner DJ, Hall EJ. Computed tomography: an increasing source of radiation exposure. N Engl J Med 2007; 357:2277–2284 5. Hall EJ, Brenner DJ. Cancer risks from diagnostic radiology: the impact of new epidemiological data. Br J Radiol 2012; 85:e1316–e1317 6. Schulze-Rath R, Hammer GP, Blettner M. Are pre- or postnatal diagnostic X-rays a risk factor for childhood cancer? A systematic review. Radiat Environ Biophys 2008; 47:301–312 7. Jones BF. A reappraisal of the use of infrared thermal image analysis in medicine. IEEE Trans Med Imaging 1998; 17:1019–1027 8. Pascoe DD, Mercer JB, de Weerd L. Physiology of thermal signals. In: Diakides NA, Bronzino JD, eds. Medical infrared imaging. Boca Raton, FL: CRC, Taylor & Francis, 2007:20 9. Awerbuch MS. Thermography: its current diagnostic status in musculoskeletal medicine. Med J Aust 1991; 154:441–444 10. Fitzgerald A, Berentson-Shaw J. Thermography as a screening and diagnostic tool: a systematic review. N Z Med J 2012; 125:80–91 11. Kennedy DA, Lee T, Seely D. A comparative review of thermography as a breast cancer screening technique. Integr Cancer Ther 2009; 8:9–16 12. Hoffman RM, Kent DL, Deyo RA. Diagnostic accuracy and clinical utility of thermography for lumbar radiculopathy: a meta-analysis. Spine (Phila Pa 1976) 1991; 16:623–628 13. Deeks JJ, Bossuyt PM, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test
accuracy, version 1.0.0. The Cochrane Collaboration website. srdta.cochrane.org/handbook-dta-reviews. Published 2009. Accessed February 25, 2014 14. Reitsma JB, Rutjes AWS, Whiting P, Vlassov VV, Leeflang MMG, Deeks JJ. Assessing methodological quality, chapter 9. In: Deeks JJ, Bossuyt PM, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test accuracy, version 1.0.0. The Cochrane Collaboration website. srdta. cochrane.org/sites/srdta.cochrane.org/files/uploads/ch09_Oct09.pdf. Published 2009. Updated October 27, 2009. Accessed February 25, 2014 15. Whiting PF, Weswood ME, Rutjes AWS, Reitsma JB, Bossuyt PNM, Kleijnen J. Evaluation of QUADAS, a tool for the quality assessment of diagnostic accuracy studies. BMC Med Res Methodol 2006; 6:9 16. Macaskill P, Gatsonis C, Deeks JJ, Harbord RM, Takwoingi Y. Analysing and presenting results, chapter 10. In: Deeks JJ, Bossuyt PM, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test accuracy, version 1.0. The Cochrane Collaboration website. srdta.cochrane.org/ sites/srdta.cochrane.org/files/uploads/Chapter%2010%20-%20Version%201.0.pdf. Published 2010. Accessed February 25, 2014 17. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to meta-analysis. Chichester, UK: Wiley, 2009 18. Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol 2006; 6:31 19. Lelik F. Capsuloligamentous injuries of the knee and cutaneous thermography (CT). Ital J Sports Traumatol 1980; 2:33–40 20. Karpman HL, Knebel A, Semel CJ, Cooper J. Clinical studies in thermography. Part 2. Application of thermography in evaluating musculoligamentous injuries of the spine: a preliminary report. Arch Environ Health 1970; 20:412–417 21. Rochcongar P, Schmitt M. Thermographic study of muscular lesions in sport (in French). J Belge Med Phys Rehabil 1979; 2:335–342 22. Guillet R, Genety J, Schmitt M. A contribution to infra-red thermography in the diagnosis of athletic muscular lesions (in French). Lyon Chir 1979; 75:7–10 23. Campailla E, Bonivento G, Plossi G. Diagnostic value of thermography for inflammatory tendon lesions in athletes (in Italian). Med Sport (Roma) 1981; 34:393–398 24. Silva CT, Naveed N, Bokhari S, et al. Early assessment of the efficacy of digital infrared thermal imaging in pediatric extremity trauma. Emerg Radiol 2012; 19:203–209 25. Bandeira F, de Moura MAM, de Souza MA, Nohama P, Neves EB. Can thermography aid in the
diagnosis of muscle injuries in soccer athletes? Rev Bras Med Esporte. 2012; 18:246–251 26. Zhang HY, Cho BY, Kim HS, Cho YE. Thermographic diagnosis of whiplash injury with/without radiculopathy. Key Eng Mater 2006; 845:321–323 27. Bernstein M, Nichols G, Blair J. The use of black and white infrared photography for recording blunt force injury. Clin Anat 2013; 26:339–346 28. Hosie KB, Wardrope J, Crosby AC, Ferguson DG. Liquid crystal thermography in the diagnosis of scaphoid fractures. Arch Emerg Med 1987; 4:117–120 29. Katz LM, Nauriyal V, Nagaraj S, et al. Infrared imaging of trauma patients for detection of acute compartment syndrome of the leg. Crit Care Med 2008; 36:1756–1761 30. Dalinka MK, Lally JF, Gohel VK. Evaluation of thermography as a screening procedure in internal derangement of the knee. Invest Radiol 1973; 8:228–232 31. Meurman K, Somer K, Sjovall J. Thermography of stress fractures in military personnel. Acta Thermograph 1980; 5:132–136 32. Devereaux MD, Parr GR, Lachmann SM, PageThomas P, Hazleman BL. The diagnosis of stress fractures in athletes. JAMA 1984; 252:531–533 33. Goodman PH, Heaslet MW, Pagliano JW, Rubin BD. Stress fracture diagnosis by computer-assisted thermography. Phys Sports Med 1985; 13:114–132 34. Schneiders AG, Sullivan SJ, Hendrick PA, et al. The ability of clinical tests to diagnose stress fractures: a systematic review and meta-analysis. J Orthop Sports Phys Ther 2012; 42:760–771 35. Knottnerus JA, Buntinx F, eds. The evidence base of clinical diagnosis: theory and methods of diagnostic research, 2nd ed. Chichester, UK: WileyBlackwell, 2009 36. Lijmer JG, Mol BW, Heisterkamp S, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA 1999; 282:1061–1066 37. Practice Guidelines Committee of the American Academy of Thermology. Guidelines for breast thermography. American Academy of Thermology website. aathermology.org/organization/guidelines/ guidelines-for-breast-thermology/. Published 2012. Accessed February 25, 2014 38. Practice Guidelines Committee of the American Academy of Thermology. Guidelines for neuromusculoskeletal thermography: infrared thermography sympathetic skin response (SSR) studies. American Academy of Thermology website. aathermology.org/organization/guidelines/guidelines-for-neuro-musculoskeletal-thermography/. Published 2012. Accessed February 25, 2014 39. Ammer K, Ring FE. Standard procedures for infrared imaging in medicine. In: Diakides NA, Bronzino JD, eds. Medical infrared imaging. Boca Raton, FL: CRC, Taylor & Francis, 2007:14 40. Ammer K. The Glamorgan protocol for recording
AJR:203, October 2014 881
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Sanchis-Sánchez et al. and evaluation of thermal images of the human body. Thermology Int 2008; 18:125–129 41. Ammer K. Repeatability of identification of hot spots in thermal images is influenced by image processing. Thermology Int 2011; 21:40–46 42. Vardasca R, Ring F, Plassmann P, Jones C. Thermal symmetry of the upper and lower extremities in healthy subjects. Thermology Int 2012; 22:53–60 43. Kolosovas-Machuca ES, González FJ. Distribution of skin temperature in Mexican children. Skin Res Technol 2011; 17:326–331 44. de Vet HCW, Eisinga A, Riphagen II, Aertgeerts B, Pewsner D. Searching for studies, chapter 7. In:
Deeks JJ, Bossuyt PM, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test accuracy, version 1.0.0. The Cochrane Collaboration website. srdta.cochrane.org/sites/srdta. cochrane.org/files/uploads/Chapter07-Searching-(September-2008).pdf. Published 2009. Accessed February 25, 2014 45. Leeflang MMG, Scholten RJPM, Rutjes AWS, Reitsma JB, Bossuyt PMM. Use of methodological search filters to identify diagnostic accuracy studies can lead to the omission of relevant studies. J Clin Epidemiol 2006; 59:234–240 46. Jones BF, Plassmann P. Digital infrared thermal
imaging of human skin. IEEE Eng Med Biol Mag 2002; 21:41–48 47. Lahiri BB, Bagavathiappan S, Jayakumar T, Philip J. Medical applications of infrared thermography: a review. Infrared Phys Technol 2012; 55:221–235 48. Saxena AK, Willital GH. Infrared thermography: experience from a decade of pediatric imaging. Eur J Pediatr 2008; 167:757–764 49. Bossuyt PM, Reitsma JB, Bruns DE, et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Clin Chem 2003; 49:7–18
F O R YO U R I N F O R M AT I O N
Unique customized medical search engine service from ARRS! ARRS GoldMiner® is a keyword- and concept-driven search engine that provides instant access to radiologic images published in peer-reviewed journals. ARRS members earn 0.5 CME credits for each self-directed search. For more information, visit goldminer.arrs.org.
882
AJR:203, October 2014
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Musculoskeletal Imaging Review
Infrared Thermal Imaging in the Diagnosis of Musculoskeletal Injuries: A Systematic Review and Meta-Analysis Enrique Sanchis-Sánchez1 Carlos Vergara-Hernández 2 Rosa M. Cibrián 3 Rosario Salvador 3 Enrique Sanchis 2 Pilar Codoñer-Franch 4 Sanchis-Sánchez E, Vergara-Hernández C, Cibrián RM, Salvador R, Sanchis E, Codoñer-Franch P
Keywords: diagnostic imaging, meta-analysis, musculoskeletal injuries, sensitivity and specificity, stress fractures, systematic review, thermography DOI:10.2214/AJR.13.11716 Received August 1, 2013; accepted after revision October 4, 2013. 1 Department of Physical Therapy, University of Valencia, C. Gasco Oliag 5, Valencia E-46010, Spain. Address correspondence to E. Sanchis-Sánchez ([email protected]). 2 Department of Electronic Engineering, University of Valencia, Valencia, Spain. 3
Department of Physiology, University of Valencia, Valencia, Spain. 4 Department of Pediatrics, Obstetrics, and Gynecology, University Hospital “Dr. Peset,” University of Valencia, Valencia, Spain.
AJR 2014; 203:875–882 0361–803X/14/2034–875 © American Roentgen Ray Society
OBJECTIVE. Musculoskeletal injuries occur frequently. Diagnostic tests using ionizing radiation can lead to problems for patients, and infrared thermal imaging could be useful when diagnosing these injuries. CONCLUSION. A systematic review was performed to determine the diagnostic accuracy of infrared thermal imaging in patients with musculoskeletal injuries. A meta-analysis of three studies evaluating stress fractures was performed and found a lack of support for the usefulness of infrared thermal imaging in musculoskeletal injuries diagnosis.
A
ccording to data collected during the year 2010, in the United States, 14,386,192 people had to attend hospital emergency departments as a result of musculoskeletal injuries, including fractures, sprains, dislocations, contusions, and compartment syndrome (International Classification of Diseases, ninth revision, Clinical Modification diagnosis codes 810–848, 920–929, and 958.9). Of those patients, 610,946 were discharged from hospital emergency departments to short-term hospitals, nursing homes, rehabilitation centers, and home health care [1], making this type of injury a frequent reason for seeking medical attention. Diagnostic imaging procedures are essential in the diagnosis of musculoskeletal injuries. These diagnostic studies usually consist of unenhanced or contrast-enhanced radiography, CT, MRI, or ultrasound. It is the clinic’s job to make the final decision as to whether these studies are appropriate in the management of each patient [2]. Moreover, the increase in the frequency of use of tests that emit ionizing radiation (e.g., in the United States, the per capita annual effective dose resulting from medical procedures increased sixfold between 1980 and 2006 [3]) could overexpose the general population [4, 5], but especially infants and unborn children [6], to unnecessary radiation doses, contradicting the “As Low As Reasonably Achievable” principle, which is based on the justification, optimization, and limitation of the dose. Infrared thermal imaging is a technique whose application in the field of health sci-
ences allows an image to be taken of the infrared radiation produced naturally by the skin, which acts like an almost perfect black body [7]. In this way, the technique can detect local variations in temperature, such as those that occur in inflammatory conditions associated with different pathologic abnormalities, including musculoskeletal injuries. Hence, it could act as a diagnostic complement by adding a physiologic profile study to the anatomic one [8] offered by traditional imaging studies, which could reduce the need for the latter, thus reducing population exposure to ionizing radiation. However, although there are reviews concerning the application of infrared thermal imaging as a diagnostic tool in health sciences [9–12], up to now we are not aware of any systematic review investigating its usefulness in the diagnosis of musculoskeletal injuries; therefore, we decided to perform this systematic review with the aim of determining the diagnostic accuracy of infrared thermal imaging for said lesions. Materials and Methods A systematic review of the diagnostic test accuracy was proposed, according to the guidelines set out by the Cochrane Collaboration [13].
Data Sources and Searches A series of electronic searches were performed using the following databases: MEDLINE (via PubMed), EMBASE (Elsevier), CINAHL (EBSCO), Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effect
AJR:203, October 2014 875
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Sanchis-Sánchez et al. (DARE), Cochrane Central Register of Controlled Trials (CENTRAL), SCOPUS, Web of Science, IME, and ICYT. No time limit was set, and everything covered in each database up to January 8, 2013, was included. The search strategy for each database was outlined around three conceptual areas (infrared thermal imaging, musculoskeletal injury, and diagnostic test accuracy), widening each of these areas as much as possible by incorporating synonyms, resulting in a set of terms in the form of a free text and terminology from the thesaurus of each database when possible. Later, the set intersection was determined, this being the end result of the entry searches. Table 1 shows the search strategy used for the MEDLINE (via PubMed) database. During the document selection process, the reference lists of the studies were reviewed as the studies were included in the review, as well as the reviews of the literature or clinical practice guides found.
TABLE 1: Search Strategy Followed in the MEDLINE (via PubMed) Database Strategy Number
Description
1
Thermography[MeSH Terms] OR “thermograph*”[Title/Abstract] OR “thermal image”[Title/ Abstract] OR “thermal imaging”[Title/Abstract] OR “infrared photography”[Title/Abstract] OR “diti”[Title/Abstract] OR “infrared image”[Title/Abstract] OR “infrared imaging”[Title/ Abstract]
2
(Wounds and injuries[MeSH Terms]) OR injuries[MeSH Subheading] OR injur*[Title/Abstract] OR trauma*[Title/Abstract] OR sprain*[Title/Abstract] OR strain*[Title/Abstract] OR fracture*[Title/Abstract] OR (“bone broke”[Title/Abstract]) OR dislocation*[Title/Abstract] OR tendinopathy[Title/Abstract] OR luxation*[Title/Abstract] OR contusion*[Title/Abstract] OR tendon*[Title/Abstract] OR (“muscle tear”[Title/Abstract])
3
diagnosis[MeSH Subheading] OR diagnos*[Text Word]
4
#1 AND #2 AND #3
lection criteria as used in the first phase were independently applied, and any conflicts between the two reviewers were resolved by the intervention of a third reviewer.
Data Extraction and Quality Assessment Study Selection Studies were included that met the following criteria: they evaluated diagnostic accuracy, offering sufficient information to allow 2 × 2 tables to be calculated (true-positives [TPs], true-negatives [TNs], false-positives [FPs], and false-negatives [FNs]), without setting development time limitations (transversal vs longitudinal) or monitoring limitations (retrospective vs prospective); they described one or several samples dealing with living human subjects, without setting any other restrictions; they made use of infrared thermal imaging (using any type of apparatus or means) in the diagnosis of musculoskeletal injuries (e.g., bone fractures, dislocations, sprains, muscle contractures, tendinopathy, contusions, or compartment syndrome); they used as a reference standard the clinical interpretation of image diagnostic exploratory tests (e.g., unenhanced or contrast-enhanced radiographs, CT, MRI, or ultrasound scanning); and they were written in English, French, Spanish, Italian, or Portuguese. The results of the searches were transferred into an electronic reference manager (RefWorks 2.0, ProQuest), eliminating duplicated entries. After this, two reviewers independently read the titles, abstracts, and keywords of the entries to determine their pertinence and relevance on the basis of the predefined criteria and to decide whether they should be included in the review. Any conflicts that arose were resolved by the intervention of a third reviewer. In the next phase, two reviewers read the complete texts of the entries included in the first phase. Because the analysis unit was the study, it was decided to reject the entries that referred to a study that had previously been included. The same se-
876
Two reviewers independently extracted the data using a template containing the identification of the study, selection criteria of the study, methodologic quality assessment, main characteristics of the study (e.g., participants, age, sex, excluded participants, losses during the study, and sampling technique), a description of the diagnostic tests and their interpretation (index test, comparator tests, and reference tests), a description of the measurements of the result, and the main results of the study (e.g., 2 × 2 table, sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio [OR], and the presence of a receiver operating characteristic [ROC] curve and, if so, the area under the curve). Information was also collected concerning the prevalence of the result, changes in the technology used, the previous concise setting of the values for a positive test, the training of the people who interpreted the diagnostic tests, the use of a controlled measurement range, and whether the subjects received treatment during the interim period between the two tests. Conflicts over extraction of data and the analysis of the methodologic quality were resolved by the intervention of a third reviewer. Methodologic quality assessment was performed according to the recommendations of the Cochrane Collaboration for systematic reviews of diagnostic test accuracy studies [14], using a template developed with the 11 items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool [15]. This was performed independently by two reviewers, with disagreements resolved by the intervention of a third reviewer. To show the results, a summary table for each of the included studies was developed, along with a figure for the set of studies included in the review.
Data Synthesis and Analysis In both document selection phases, the interrater reliability was calculated by means of the kappa index (95% CI computed with 2000 bootstrap samples [adjusted bootstrap percentile]). The necessary information to build 2 × 2 tables for each study was collected, and the data to calculate estimations of sensitivity (TP / [TP + FN]) and specificity (TN / [TN + FP]) (with 95% CI) from the index test were extracted. These were then represented on a forest plot. We decided to perform a meta-analysis for each result and to estimate a combined value only when the absence of high heterogeneity and inconsistency between the studies included could be corroborated. Heterogeneity was evaluated with the chisquare test, and inconsistency was evaluated with the Higgins I-square test. We decided not to provide a combined presentation of the results when the heterogeneity showed statistically significant results and when the inconsistency was over 75%. For the presentation of the results, we decided to choose between a point estimate of the pooled values in the meta-analysis (by means of a forest plot) or a presentation in the ROC space using a summary ROC curve [16]. The decision to use one of them was based on finding variability in the diagnostic threshold, which was assessed by means of the Spearman correlation coefficient between the logit of sensitivity and the logit of 1 − specificity. We decided that when a statistically significant variability in the diagnostic threshold was identified, the results were pooled by means of a summary ROC curve. To identify possible sources of heterogeneity and to perform a stratified meta-analysis that excluded these sources (as long as the number of studies was sufficiently high [17]), a meta-regression by means of a generalization of Littenberg and Moses linear model was planned. All the statistical analyses were performed with a significance level of α = 0.05, using MetaDiSc software (version 1.4) [18] and R software
AJR:203, October 2014
Infrared Thermal Imaging of Musculoskeletal Injuries
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
(version 3.0.0; packages mada, psy, and boot; The R Foundation for Statistical Computing).
Fig. 1—Flow diagram of studies considered for review.
1727 Records identified through electronic database searching
Results Figure 1 shows the flow diagram of the studies considered in the review. After we performed electronic bibliographic searches in the databases, a total of 1727 search results were found, and no results from any other data sources were found. After eliminating 318 duplicated search results, 1409 results were subjected to the first narrowing down process, and 1393 results were eliminated after we read their title, abstract, or keywords because of a lack of relevance with regard to the established selection criteria. Among the 16 articles that were read through fully to consider their eligibility, none of them was found to make reference to a single study; hence, they were taken to be independent studies. Ten of these studies were finally excluded: eight because they did not conform to a methodologic design to evaluate diagnostic accuracy [9, 19–25], one because it did not use an appropriate reference standard [26], and the last one because it was a
0 Records identified through other sources
1409 Records after duplicates removed
Not relevant: 1393 Records excluded
1409 Records screened for title, abstract, and keywords
10 Articles excluded: • Study design: 8 • Index test: 0 • Reference standard: 1 • Outcome: 0 • Population: 1
16 Full-text articles accessed for eligibility
6 Studies included in qualitative synthesis
3 Studies excluded from the quantitative synthesis (meta-analysis): • Unable to extract data: 2 • Wide outcome: 1
3 Studies included in quantitative synthesis (meta-analysis)
TABLE 2: Characteristics of the Included Studies Dalinka et al. [30]
Characteristic Number of subjects
83
Meurman et al. [31] 84
Devereaux et al. [32] 18
Goodman et al. [33] 17
Hosie et al. [28] 50
Katz et al. [29] 164
Design
Prospective
Prospective
Prospective
Prospective
Prospective
Prospective
Setting
Not stated
Military recruits
Athletes
Athletes
Emergency department patients
Emergency department patients
Reason for presentation
Suspected problem Suspected stress fracture
Suspected stress fracture
Suspected stress fracture
Scaphoid fracture diagnosed
Suspected compartment syndrome
Mean age of patients (y)
Not stated
20.6
23
Not stated
Not stated
36
Male patients (%)
Not stated
100
61
59
Not stated
Not stated
Outcome prevalence (%)
69
76
83
65
100
7
Camera
Tektronix
Aga-Thermovision 680 Medical
Not stated
Dorex
Not stated
L-Wave infrared camera
Camera calibration
Not stated
Not stated
Not stated
Not stated
Not stated
Yes
Image quality assessment
Not stated
Not stated
Not stated
Not stated
Not stated
Yes
Image analysis
Visual: temperature Visual: temperature Not stated asymmetry asymmetry
Computerized: area Visual: temperature Computerized: and temperature asymmetry temperature asymmetry asymmetry
Interrater reliability (intraclass Not stated correlation coefficient)
Not stated
Not stated
Not stated
Not stated
0.98
Acclimation time (min)
10
15
10
Not stated
Not stated
10
Region of interest selection
Not stated
Yes
Yes
Yes
Yes
Yes
Distance (m)
Not stated
Not stated
1
Not stated
Not applicable
0.9
Researchers training
Not stated
Not stated
Not stated
Not stated
Not stated
Yes
Environment temperature (°C)
20
Not stated
20
Not described
Not stated
Not stated
Relative humidity (%)
Not stated
Not stated
Not stated
Not stated
Not stated
Not stated
AJR:203, October 2014 877
Sanchis-Sánchez et al. TABLE 3: Methodologic Quality Summary of the Included Studies According to the Items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) Tool
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
QUADAS Item
Dalinka et al. [31]
Meurman et al. [31] Devereaux et al. [32] Goodman et al. [33]
Hosie et al. [28]
Katz et al. [29]
Withdrawals
No, low quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Noninterpretable results
No, low quality
Unclear quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Clinical review
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Diagnostic review
Yes, high quality
Unclear quality
Unclear quality
Unclear quality
Unclear quality
Yes, high quality
Test review
Yes, high quality
Unclear quality
Unclear quality
Unclear quality
Unclear quality
Yes, high quality
Incorporation
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Differential verification
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Partial verification
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Disease progression
Unclear quality
Unclear quality
Unclear quality
Unclear quality
No, low quality
Yes, high quality
Reference standard
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Yes, high quality
Patients’ spectrum
Yes, high quality
Yes, high quality
Yes, high quality
No, low quality
Unclear quality
Yes, high quality
study using corpses [27]. The interrater reliability for the first narrowing down study selection process was κ = 0.702 (p < 0.01; 95% CI, 0.476–0.848), whereas for the second one it was κ = 0.871 (p < 0.01; 95% CI, 0.4–1). Characteristics of the six studies included in the review are shown in Table 2. In two of them [28, 29], it was not possible to extract the necessary information to build a 2 × 2 table despite the fact that the authors reported information about the diagnostic accuracy of infrared thermal imaging on a total of 214 patients (these studies investigated scaphoid bone fractures and compartment syndrome, respectively). Despite efforts made to contact the authors of these studies, no replies were received; therefore, they could not be included in the quantitative synthesis. An analysis of the methodologic quality of the six studies included (Table 3) found a low bias risk (i.e., a low risk for all the items on the QUADAS tool) for the study by Katz
et al. [29] and a moderate-to-high risk for the remaining studies. In a detailed analysis, four studies used consecutive sampling on a range of patients [29–32], one did not describe the sampling technique used [28], and another described convenience sampling [33]. All the studies used an appropriate reference standard, which varies from the interpretation of unenhanced and contrastenhanced radiographic techniques, to the combination of these techniques with a meticulous physical examination. With regard to the time period between the application of the reference standard test and the index test, only one study [29] provided adequate information, four studies were not very clear on this point [30–33], and one gave no information whatsoever [28]. The use of blinding techniques between the results of the index test and the reference standard are not clear in four of the six studies [28, 31–33]. One study did not adequately clarify wheth-
er there were noninterpretable results [31], whereas another [30] showed a clear disparity in the form of noninterpretable results and unexplained losses. Hosie et al. [28] studied infrared thermal imaging in the diagnosis of scaphoid bone fractures, reporting a sensitivity of 0.77 (with three FN results), a specificity of 0.82 (with seven FP results), and a negative predictive value of 0.9 (outcome prevalence, 100%). Because there was no 2 × 2 table, an attempt was made to calculate the TP and TN rates, but the values obtained did not match those given by the authors. Katz et al. [29] studied infrared thermal imaging in the diagnosis of compartment syndrome, and they reported the presence of an ROC curve and presented an estimated area under the curve value that was equal to 1 when comparing the temperature of the thigh with that of the foot and equal to 0.98 when only taking into account the temperature of
TABLE 4: Diagnostic Performance of Infrared Thermal Imaging in the Diagnosis of Stress Fractures
Study
No. of True False False True Sensitivity (CI Patients Positive Positive Negative Negative 95%)
Specificity (CI 95%)
Positive Negative Likelihood Ratio Likelihood Ratio Diagnostic Odds (CI 95%) (CI 95%) Ratio (CI 95%)
Meurman et al. [31]
84
29
8
35
12
0.453 (0.328–0.583)
0.6 (0.361–0.809)
1.13 (0.62–2.06)
0.911 (0.598–1.389)
1.243 (0.448–3.45)
Devereaux et al. [32]
18
12
0
3
3
0.8 (0.519–0.957)
1 (0.292–1)
6.25 (0.46–84.63)
0.25 (0.092–0.678)a
25 (1.028–608.10)a
Goodman et al. [33]
17
9
1
2
5
0.82 (0.482–0.977)
0.833 (0.359–0.996)
4.91 (0.8–30.02)
0.218 (0.059–0.804)a
22.5 (1.609–314.57)a
10.05 (p < 0.01)
3.6 (p = 0.17)
4.22 (p = 0.12)
8.73 (p = 0.01)
6.48 (p = 0.04)
80.1
44.4
52.5
77.1
69.1
Heterogeneityb Inconsistency (%)c
Note—We added 0.5 to all cells of the studies with zero. Stress fracture prevalence was 76% in Meurman et al. [31], 83% in Devereaux et al. [32], and 65% in Goodman et al [33]. aThe value of 1 is not between the values of the 95% CI. bAssessed through the chi-square test (degrees of freedom, 2). cAssessed through the Higgins I-square test.
878
AJR:203, October 2014
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Infrared Thermal Imaging of Musculoskeletal Injuries the foot (outcome prevalence, 7%). However, no information was given about either the 95% CI or the FP, FN, and TN rates, and these could not be calculated by the reviewers because they did not have the raw research data. Dalinka et al. [30] studied a sample of 83 research subjects (outcome prevalence, 60%), reporting a sensitivity of 0.74 (95% CI, 0.61–0.85) and a specificity of 0.44 (95% CI, 0.24–0.65). However, the result assessed was too broad because it grouped together diverse knee pathologic abnormalities without differentiating among them in the analysis phase; therefore, it could not be included in the quantitative synthesis. Meurman et al. [31], Devereaux et al. [32], and Goodman et al. [33] investigated the same pathologic abnormalities (stress fractures), including a total of 119 research subjects, 90 of whom developed stress fractures. These three studies were included in the quantitative synthesis. Table 4 shows the diagnostic performance values of infrared thermal imaging. With regard to heterogeneity, the chisquare test was statistically significant for sensitivity (χ2 = 10.05; degrees of freedom [df] = 2; p < 0.01), negative likelihood ratio (χ2 = 8.73; df = 2; p = 0.01), and diagnostic OR (χ2 = 6.48; df = 2; p = 0.04), but was not significant for specificity (χ2 = 3.60; df = 2; p = 0.17) and positive likelihood ratio (χ2 = 4.21; df = 2; p = 0.12). High inconsistency was found for sensitivity (Higgins I2 = 80.1%), negative likelihood ratio (Higgins I2 = 77.1%), and diagnostic OR (Higgins I2 = 69%), and moderate inconsistency was found for specificity (Higgins I2 = 44.4%) and positive likelihood ratio (Higgins I2 = 52.5%). It was considered inappropriate to perform a meta-regression to explore the sources of heterogeneity because of the low number of studies found. Because of all these factors, we decided to present a combined result only for specificity and positive likelihood ratio. The Spearman correlation coefficient between the logit of sensitivity and the logit of 1 − specificity was not significant (ρ = −0.5; p = 0.67), so there is no conclusive proof of the existence of variability in the diagnostic threshold between the studies; thus, we decided to pool results only by means of a forest plot. Figure 2 shows the forest plots for the sensitivity, specificity, and likelihood ratios of the studies that were included in the quantitative analysis. In Figure 2, you can see the high variability for sensitivity and negative likelihood ratio among the studies of Meurman
Sensitivity (95% CI) Meurman et al. [31] Devereaux et al. [32] Goodman et al. [33]
0
0.2
0.4 0.6 Sensitivity
0.45 (0.33 − 0.58) 0.80 (0.52 − 0.96) 0.82 (0.48 − 0.98)
Chi-square = 10.05; df = 2 (p < 0.01) 1 Inconsistency (I-square) = 80.1%
0.8
A Specificity (95% CI) Meurman et al. [31] Devereaux et al. [32] Goodman et al. [33]
0
0.2
0.4 0.6 Specificity
0.60 (0.36 − 0.81) 1.00 (0.29 − 1.00) 0.83 (0.36 − 1.00)
Pooled Specificity = 0.69 (0.49 to 0.85) Chi-square = 3.60; df = 2 (p = 0.1656) 1 Inconsistency (I-square) = 44.4%
0.8
B Positive Likelihood Ratio (95% CI) Meurman et al. [31] 1.13 (0.62 − 2.07) Devereaux et al. [32] 6.25 (0.46 − 84.63) Goodman et al. [33] 4.91 (0.80 − 30.02)
0.01
1 Positive Likelihood Ratio
Random Effects Model Pooled Positive Likelihood Ratio = 2.31 (0.63 to 8.47) Chi-square = 4.21; df = 2 (p = 0.1216) 100.0 Inconsistency (I-square) = 52.5% Tau-squared = 0.7156
C Negative Likelihood Ratio (95% CI) Meurman et al. [31] Devereaux et al. [32] Goodman et al. [33]
0.01
1 Negative Likelihood Ratio
0.91 (0.60 − 1.39) 0.25 (0.09 − 0.68) 0.22 (0.06 − 0.80)
Chi-square = 8.73; df = 2 (p = 0.01) 100.0 Inconsistency (I-square) = 77.1% Tau-squared = 0.6619
D Fig. 2—Forest plots of data from diagnostic test accuracy studies of infrared thermal imaging in diagnosis of stress fractures. A–D, Graphs show sensitivities (A), specificities (B), positive likelihood ratio (C), and negative likelihood ratio (D).
et al. [31] (0.45 [95% CI, 0.33–5.58] and 0.91 [95% CI, 0.6–1.39], respectively), Devereaux et al. [32] (0.80 [95% CI, 0.52–0.96] and 0.25 [95% CI, 0.09–0.68], respectively), and Goodman et al. [33] (0.82 [95% CI, 0.48–0.98] and 0.22 [95% CI, 0.06–0.80], respectively). Although the values for specificity and positive likelihood ratio were 0.60
(95% CI, 0.36–0.81) and 1.13 (95% CI, 0.62– 2.07) , respectively, in the study by Meurman et al. [31], 0.83 (95% CI, 0.36–1.00) and 4.91 (95% CI, 0.80–30.02), respectively, in Goodman et al. [33], and 1.00 (95% CI, 0.29–1.00) and 6.25 (95% CI, 0.46–84.63), respectively, in Devereaux et al. [32], the combined estimate of specificity was 0.69 (95% CI, 0.49–
AJR:203, October 2014 879
Sanchis-Sánchez et al.
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
0.85) and the positive likelihood ratio was 2.31 (95% CI, 0.63–8.47), thus encompassing the 1 value between the 95% CIs. Discussion Despite the methodologic limitations found in the included studies, our results show that infrared thermal imaging has a moderate diagnostic accuracy for stress fractures. Although there are promising results regarding the role of infrared thermal imaging in the diagnosis of compartmental syndrome, nothing conclusive can be inferred about the role of infrared thermal imaging for the remaining musculoskeletal injuries. The results of a recent systematic review concerning the diagnosis of stress fractures by means of ultrasound and tuning forks [34] shows results similar to those offered by infrared thermal imaging in this review (pooled results for ultrasounds: sensitivity, 0.642 [95% CI, 0.548–0.727]; specificity, 0.631 [95% CI, 0.542–0.712]; positive likelihood ratio, 2.092 [95% CI, 1.099– 3.517]; negative likelihood ratio, 0.35 [95% CI, 0.079–0.861]; and diagnostic OR, 6.199 [95% CI, 0.697–22.75]), with the advantage that infrared thermal imaging does not produce pain for the patient, in contrast with the aforementioned procedures. However, the results pooled in the meta-analysis (performed only for ultrasounds) undertaken by Schneiders et al. [34] may not be consistent because of the absence of heterogeneity assessment, especially when there seems to be a certain discrepancy between the results provided in the forest plots for sensitivity, specificity, negative likelihood ratio, and diagnostic OR. The low number of studies found, the methodologic differences, and the unexplained heterogeneity found in the different studies mean that the results obtained have to be viewed with caution. These differences could have arisen because of diverse factors that mainly pivot around an inadequate design of a diagnostic accuracy study [35], such as a sample with an excessively high results prevalence, the lack of blind testing in the results for both the index test and the reference standard, lack of a description of the measuring protocol, and the analysis, especially the way in which the collected information was adjusted as much as possible to reality, whether by controlling the variables of the surroundings or by using a visual rather than computerized analysis (Table 2). All these factors can lead to a bi-
880
ased estimate of the diagnostic accuracy of infrared thermal imaging [36]. With regard to the features of infrared cameras, the age of the equipment used, the fact that the studies were quite old, and the type of data analysis all could very well explain the low values of diagnostic accuracy. It is noteworthy that several of the studies included in the review do not describe the cameras used [28, 32]. They do not mention their technical characteristics or calibration with regard to a black body, nor do they evaluate their image quality [28, 30–33]. This limited information concerning the cameras could lead to a biased interpretation of the results. Although there are guidelines that contain recommendations for performing a study that uses infrared thermal imaging as a diagnostic tool [37–41] (e.g., some factors to be considered when performing infrared thermal imaging are medication, waiting time, atmospheric temperature, cutaneous products, history of vasomotor problems, sensitivity of the camera, relative humidity, direct ventilation, existence of a measurement protocol, measurement angle, distance, training, conduct, and general state of the subjects), to our knowledge, no study directly refers to these factors. In the studies we reviewed, only some of the factors that are described in the guidelines were controlled; therefore, the measurements could be yielding biased values. Limitations of the Review The synthesis of the quantitative results could have been limited by combining injuries that occurred in different parts of the body. However, there is research suggesting that the acceptance of equality of temperature between opposing sides of the body is valid [42, 43], which minimizes the possibility of variation due to this combination. Because the reviewers did not always agree with regard to the selection of the articles and studies for inclusion in the review, this may lead one to think that the criteria for selection and exclusion were not correctly followed. However, the high values given by the kappa index show that there was a high rate of agreement, reducing this possibility. The establishment of language limits as selection criteria could have led to a potential selection bias, but taking into account the combined coverage of the various databases consulted, we think that this has been minimized to a great extent.
Despite the fact that the Cochrane Collaboration [44] and other authors [45] do not recommend the systematic use of methodologic filters as part of the search strategy for diagnostic test accuracy systematic reviews, we decided to use a generic filter (third conceptual combination in the search strategy) as a consequence of the wide range of results that proved to be of interest with regard to the aims of the review. Implications for Practice Regarding the role of infrared thermal imaging as a diagnostic tool for musculoskeletal injuries, the few studies found (this meant that the Spearman correlation coefficient between the logit of sensitivity and the logit of 1 − specificity was not statistically significant because of a lack of power); the high heterogeneity and inconsistency for sensitivity, negative likelihood ratio, and diagnostic OR, which ruled out their combination; the low combined specificity value (0.69 [95% CI, 0.49–0.85]) and positive likelihood ratio (2.31 [95% CI, 0.63–8.47]); and the low methodologic quality mean that there is no convincing evidence of its clinical utility. Thus, for the time being, infrared thermal imaging should not be incorporated into clinical routines. Implications for Future Research Since the end of the 1990s, infrared thermal imaging technology has evolved, becoming much more accurate and reliable [7, 46]; this, along with the results offered regarding its use in other types of injuries [47], should lead to the prospect of future research. In addition, the inherent values of infrared thermal imaging itself (it is a fast, direct, noninvasive, not overcomplicated, and relatively moderately priced method) would suggest an increase in the possibilities and usefulness of this technique. This is something that should be investigated in the future because of its potential implications for the improvement of health care. An area where infrared thermal imaging could be extremely useful would be in the diagnosis of injuries in populations who are especially vulnerable to ionizing radiation, such as children, for whom it has already been studied not only for musculoskeletal injuries [24] but also for other pathologic abnormalities [48], or pregnant women. However, future research must adequately address the most problematic aspects
AJR:203, October 2014
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Infrared Thermal Imaging of Musculoskeletal Injuries that have been identified in this review to clarify the usefulness of infrared thermal imaging as a diagnostic tool (selection of research subjects, prevalence of result values, or the technical characteristics, calibration, and protocol for the acquisition of images with the equipment). To do this, it will be necessary to systematically follow the STARD Statement checklist [49], as well as the specific guidelines concerning infrared thermal imaging [37–41]. References 1. Healthcare Cost and Utilization Project (HCUP). Overview of the state emergency department databases (SEDD). HCUP website. www.hcup-us. ahrq.gov/seddoverview.jsp. Published 2010. Accessed February 25, 2014 2. Sistrom CL. The appropriateness of imaging: a comprehensive conceptual framework. Radiology 2009; 251:637–649 3. Mettler FA, Bhargavan M, Faulkner K, et al. Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources—1950–2007. Radiology 2009; 253:520–531 4. Brenner DJ, Hall EJ. Computed tomography: an increasing source of radiation exposure. N Engl J Med 2007; 357:2277–2284 5. Hall EJ, Brenner DJ. Cancer risks from diagnostic radiology: the impact of new epidemiological data. Br J Radiol 2012; 85:e1316–e1317 6. Schulze-Rath R, Hammer GP, Blettner M. Are pre- or postnatal diagnostic X-rays a risk factor for childhood cancer? A systematic review. Radiat Environ Biophys 2008; 47:301–312 7. Jones BF. A reappraisal of the use of infrared thermal image analysis in medicine. IEEE Trans Med Imaging 1998; 17:1019–1027 8. Pascoe DD, Mercer JB, de Weerd L. Physiology of thermal signals. In: Diakides NA, Bronzino JD, eds. Medical infrared imaging. Boca Raton, FL: CRC, Taylor & Francis, 2007:20 9. Awerbuch MS. Thermography: its current diagnostic status in musculoskeletal medicine. Med J Aust 1991; 154:441–444 10. Fitzgerald A, Berentson-Shaw J. Thermography as a screening and diagnostic tool: a systematic review. N Z Med J 2012; 125:80–91 11. Kennedy DA, Lee T, Seely D. A comparative review of thermography as a breast cancer screening technique. Integr Cancer Ther 2009; 8:9–16 12. Hoffman RM, Kent DL, Deyo RA. Diagnostic accuracy and clinical utility of thermography for lumbar radiculopathy: a meta-analysis. Spine (Phila Pa 1976) 1991; 16:623–628 13. Deeks JJ, Bossuyt PM, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test
accuracy, version 1.0.0. The Cochrane Collaboration website. srdta.cochrane.org/handbook-dta-reviews. Published 2009. Accessed February 25, 2014 14. Reitsma JB, Rutjes AWS, Whiting P, Vlassov VV, Leeflang MMG, Deeks JJ. Assessing methodological quality, chapter 9. In: Deeks JJ, Bossuyt PM, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test accuracy, version 1.0.0. The Cochrane Collaboration website. srdta. cochrane.org/sites/srdta.cochrane.org/files/uploads/ch09_Oct09.pdf. Published 2009. Updated October 27, 2009. Accessed February 25, 2014 15. Whiting PF, Weswood ME, Rutjes AWS, Reitsma JB, Bossuyt PNM, Kleijnen J. Evaluation of QUADAS, a tool for the quality assessment of diagnostic accuracy studies. BMC Med Res Methodol 2006; 6:9 16. Macaskill P, Gatsonis C, Deeks JJ, Harbord RM, Takwoingi Y. Analysing and presenting results, chapter 10. In: Deeks JJ, Bossuyt PM, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test accuracy, version 1.0. The Cochrane Collaboration website. srdta.cochrane.org/ sites/srdta.cochrane.org/files/uploads/Chapter%2010%20-%20Version%201.0.pdf. Published 2010. Accessed February 25, 2014 17. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to meta-analysis. Chichester, UK: Wiley, 2009 18. Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol 2006; 6:31 19. Lelik F. Capsuloligamentous injuries of the knee and cutaneous thermography (CT). Ital J Sports Traumatol 1980; 2:33–40 20. Karpman HL, Knebel A, Semel CJ, Cooper J. Clinical studies in thermography. Part 2. Application of thermography in evaluating musculoligamentous injuries of the spine: a preliminary report. Arch Environ Health 1970; 20:412–417 21. Rochcongar P, Schmitt M. Thermographic study of muscular lesions in sport (in French). J Belge Med Phys Rehabil 1979; 2:335–342 22. Guillet R, Genety J, Schmitt M. A contribution to infra-red thermography in the diagnosis of athletic muscular lesions (in French). Lyon Chir 1979; 75:7–10 23. Campailla E, Bonivento G, Plossi G. Diagnostic value of thermography for inflammatory tendon lesions in athletes (in Italian). Med Sport (Roma) 1981; 34:393–398 24. Silva CT, Naveed N, Bokhari S, et al. Early assessment of the efficacy of digital infrared thermal imaging in pediatric extremity trauma. Emerg Radiol 2012; 19:203–209 25. Bandeira F, de Moura MAM, de Souza MA, Nohama P, Neves EB. Can thermography aid in the
diagnosis of muscle injuries in soccer athletes? Rev Bras Med Esporte. 2012; 18:246–251 26. Zhang HY, Cho BY, Kim HS, Cho YE. Thermographic diagnosis of whiplash injury with/without radiculopathy. Key Eng Mater 2006; 845:321–323 27. Bernstein M, Nichols G, Blair J. The use of black and white infrared photography for recording blunt force injury. Clin Anat 2013; 26:339–346 28. Hosie KB, Wardrope J, Crosby AC, Ferguson DG. Liquid crystal thermography in the diagnosis of scaphoid fractures. Arch Emerg Med 1987; 4:117–120 29. Katz LM, Nauriyal V, Nagaraj S, et al. Infrared imaging of trauma patients for detection of acute compartment syndrome of the leg. Crit Care Med 2008; 36:1756–1761 30. Dalinka MK, Lally JF, Gohel VK. Evaluation of thermography as a screening procedure in internal derangement of the knee. Invest Radiol 1973; 8:228–232 31. Meurman K, Somer K, Sjovall J. Thermography of stress fractures in military personnel. Acta Thermograph 1980; 5:132–136 32. Devereaux MD, Parr GR, Lachmann SM, PageThomas P, Hazleman BL. The diagnosis of stress fractures in athletes. JAMA 1984; 252:531–533 33. Goodman PH, Heaslet MW, Pagliano JW, Rubin BD. Stress fracture diagnosis by computer-assisted thermography. Phys Sports Med 1985; 13:114–132 34. Schneiders AG, Sullivan SJ, Hendrick PA, et al. The ability of clinical tests to diagnose stress fractures: a systematic review and meta-analysis. J Orthop Sports Phys Ther 2012; 42:760–771 35. Knottnerus JA, Buntinx F, eds. The evidence base of clinical diagnosis: theory and methods of diagnostic research, 2nd ed. Chichester, UK: WileyBlackwell, 2009 36. Lijmer JG, Mol BW, Heisterkamp S, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA 1999; 282:1061–1066 37. Practice Guidelines Committee of the American Academy of Thermology. Guidelines for breast thermography. American Academy of Thermology website. aathermology.org/organization/guidelines/ guidelines-for-breast-thermology/. Published 2012. Accessed February 25, 2014 38. Practice Guidelines Committee of the American Academy of Thermology. Guidelines for neuromusculoskeletal thermography: infrared thermography sympathetic skin response (SSR) studies. American Academy of Thermology website. aathermology.org/organization/guidelines/guidelines-for-neuro-musculoskeletal-thermography/. Published 2012. Accessed February 25, 2014 39. Ammer K, Ring FE. Standard procedures for infrared imaging in medicine. In: Diakides NA, Bronzino JD, eds. Medical infrared imaging. Boca Raton, FL: CRC, Taylor & Francis, 2007:14 40. Ammer K. The Glamorgan protocol for recording
AJR:203, October 2014 881
Downloaded from www.ajronline.org by Iowa State University on 09/28/14 from IP address 129.186.1.55. Copyright ARRS. For personal use only; all rights reserved
Sanchis-Sánchez et al. and evaluation of thermal images of the human body. Thermology Int 2008; 18:125–129 41. Ammer K. Repeatability of identification of hot spots in thermal images is influenced by image processing. Thermology Int 2011; 21:40–46 42. Vardasca R, Ring F, Plassmann P, Jones C. Thermal symmetry of the upper and lower extremities in healthy subjects. Thermology Int 2012; 22:53–60 43. Kolosovas-Machuca ES, González FJ. Distribution of skin temperature in Mexican children. Skin Res Technol 2011; 17:326–331 44. de Vet HCW, Eisinga A, Riphagen II, Aertgeerts B, Pewsner D. Searching for studies, chapter 7. In:
Deeks JJ, Bossuyt PM, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test accuracy, version 1.0.0. The Cochrane Collaboration website. srdta.cochrane.org/sites/srdta. cochrane.org/files/uploads/Chapter07-Searching-(September-2008).pdf. Published 2009. Accessed February 25, 2014 45. Leeflang MMG, Scholten RJPM, Rutjes AWS, Reitsma JB, Bossuyt PMM. Use of methodological search filters to identify diagnostic accuracy studies can lead to the omission of relevant studies. J Clin Epidemiol 2006; 59:234–240 46. Jones BF, Plassmann P. Digital infrared thermal
imaging of human skin. IEEE Eng Med Biol Mag 2002; 21:41–48 47. Lahiri BB, Bagavathiappan S, Jayakumar T, Philip J. Medical applications of infrared thermography: a review. Infrared Phys Technol 2012; 55:221–235 48. Saxena AK, Willital GH. Infrared thermography: experience from a decade of pediatric imaging. Eur J Pediatr 2008; 167:757–764 49. Bossuyt PM, Reitsma JB, Bruns DE, et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Clin Chem 2003; 49:7–18
F O R YO U R I N F O R M AT I O N
Unique customized medical search engine service from ARRS! ARRS GoldMiner® is a keyword- and concept-driven search engine that provides instant access to radiologic images published in peer-reviewed journals. ARRS members earn 0.5 CME credits for each self-directed search. For more information, visit goldminer.arrs.org.
882
AJR:203, October 2014
