Acta Oto-Laryngologica
ISSN: 0001-6489 (Print) 1651-2251 (Online) Journal homepage: http://www.tandfonline.com/loi/ioto20
‘Carnisel’: An Expert System for Vestibular Diagnosis CÉSar Gavilán, JosÉ Gallego & Javier Gavilán To cite this article: CÉSar Gavilán, JosÉ Gallego & Javier Gavilán (1990) ‘Carnisel’: An Expert System for Vestibular Diagnosis, Acta Oto-Laryngologica, 110:3-4, 161-167, DOI: 10.3109/00016489009122532 To link to this article: http://dx.doi.org/10.3109/00016489009122532
Published online: 08 Jul 2009.
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Date: 10 May 2016, At: 01:56
Acta Otolaryngol (Stockh) 1990; 110: 161-167
‘Carrusel’: An Expert System for Vestibular Diagnosis CESAR GAVILAN,’ JOSE GALLEGO* and JAVIER GAVILAN’ From the ‘Department of Otorhinolaryngology, Ln Par Hospitnl, Autonomous University, and the *System Diuision of Rank Xerox, Madrid, Spnin
Downloaded by [Australian National University] at 01:56 10 May 2016
Gavilan C, Gallego J, Gavilan J. ‘Carrusel’: An expert system for vestibular diagnosis. Acta Otolaryngol (Stockh) 1990; 110: 161-167. Neuro-otology is the only field of otolaryngology in which diagnoses are mainly deductive. Although many technological advances have indeed occurred, the patient history still remains the most important part of the evaluation of a patient complaining of vertigo and/or dizziness. The use of artificial intelligence methods as an aid for the solution of clinical problems is not new. “Carrusel” is a Prolog build-up expert system dealing with the diagnosis of vestibular disorders that achieves a success rate of 97 % when compared to the human experts involved in its design. Key words: artiJcinl intelligence, expert system, vestibular diagnosis, neuro-otology .
INTRODUCTION Otorhinolaryngology is a specialty in which diagnoses are mainly based on visual methods and techniques and radiological, anatomical or pathological confirmation is frequent. However, accurate diagnoses of vestibular disorders, unlike the rest of ENT diagnoses, are based on deductive clinical data. It is seldom possible to reach clinical confirmation by means of complementary exploratory techniques, and the diagnostic doubt is always present. Here artificial intelligence has a special application. Artificial intelligence has already been applied to other fields in medicine in the form of expert systems (1-5). Vestibular disorders, because of their complexity and lack of diagnostic confirmation, may also profit from an expert, or knowledge-based system. In 1987 we began to create such an expert system, ‘Carrusel’. One year later we presented it at an international meeting and an overview of the system was published (4). Simultaneously, another expert system for the classification and diagnosis of dizziness was reported by another group (5). ‘Carrusel’ is now being validated by several international otoneurologists who were not involved in its creation. DIAGNOSTIC APPROACH ‘Carrusel’ is an expert system written in Prolog which runs in 640 Kb PC compatible computers. It assists in the diagnosis of all diseases related both to vertigo and dizziness, either peripheral or central and affecting the vestibular and/or the oculomotor system. Two independent stages integrate the diagnostic procedure. During the first-presumption diagnosis-‘Carrusel’ suggests the most probable diagnoses on the basis of the data provided by the patient history. The clinical tests needed to confirm these diagnoses are requested by the own expert system. A clinical history number is generated at the end of this stage to be used when the requested exploratory clinical data becomes available. During the second stage-confirmation diagnosis-‘Carrusel’ asks for the results of the clinical tests requested in the first stage and integrates them into the diagnostic model. Presumption diagnosis
The first diagnostic approach gives one or more suspected diagnoses deduced from the patient history data collected through a computer-controlled questionnaire. The reasoning 11-908405
Downloaded by [Australian National University] at 01:56 10 May 2016
162 C . Gavilrin et al.
Acta Otolaryngol (Stockh) 110
process is based on the diagnosis by medical history previously reported (6, 7). According to this method three different groups are considered under the names of vertigo, dizziness and dysequilibrium. The first group includes all diseases having true vertigo as a major symptom. The second group contains the disorders characterized by a vague non-specific feeling called dizziness. The latter group includes central disorders in which dysequilibrium without vertigo is the main symptom. This third group is not comprised in the presumption diagnostic process because the diagnosis of the disorders of the central nervous system is mainly based on data provided by the neurootological examination rather than from the patient history. Central disorders are included in the second stageconfirmation diagnosis-when the results of the clinical tests are requested to confirm the proposed diagnosis. For the first two groups ‘Carrusel’ tries to find a clinical diagnosis. For the central group it only attempts to get a topographic localization of the lesion, without considering the clinical name of the disease for the most part of the cases. The part dealing with diseases related to vertigo and dizziness without central nervous system participation has already been debugged and tested. In this section there are 36 different possible diagnoses (Table I) which access more than 2 300 rules or meta-rules. Two different kinds of rules are present within ‘Carrusel’: discriminant rules and confirming rules. Discriminant rules contain mandatory symptoms unavoidable for a given diagnosis. Confirming rules substantiate or unsubstantiate the presumption diagnosis and therefore are not unavoidable. In relation to the specific weight of the selected rules, all the diagnoses proposed during the first round are ordered from greater to lesser probability according to a confidence factor. Even though the number of rules to diagnose ratio is very high, sometimes the symptoms provided by the patient in his first visit may not fit any of the rules. This may be due either to the atypical appearance of the disease, to its short evolution, or to the lack of the patient’s cooperation when exposing his or her symptoms. If ‘Carrusel’ is not able to find any diagnosis in the first round, it will automatically begin a second loop in which the discriminant rules are very few and the confirming rules many. Since the importance of the certainty factor mainly depends on the discriminant rules, the confidence factor of the diagnoses obtained by this process will be lower than the one obtained in the first loop.
Table I . Clinical diagnoses as considered by ‘Carrusel’ Meniere’s syndrome Pseudo-Meniere’s syndrome (8. 9) Sudden vestibular loss (10) Sudden cochleo-vestibular loss Vestibular neuronitis (1 I ) Positional paroxysmal vertigo Labyrinthine fracture Serous labyrinthitis Suppurative labyrinthitis Labyrinthine fistula Perilymphatic fistula Central nuclear syndrome Otosclerosis with vertigo Ototoxic syndrome Single unidentifiable bout Atypical vestibular episodes Subclavian steal syndrome Postconcussion positional vertigo
Postconcussion syndrome Psychogenic syndrome Acrophobia Agoraphobia Motion sickness Malinger Diffuse cerebral ischemia Syncope Peripheral idiopathic degeneration Vertebrobasilar insufficiency Iatrogenic (surgical) trauma Cerebello pontine angle syndrome Orthostatic syndrome Cervical syndrome Cogan’s syndrome Drop attack VertigoiHeadache VIIIth nerve syndrome
Acta Otolaryngol (Stockh) 1 I0
‘Carrusel’:An expert system
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When the symptoms provided by the patient are not very typical more than one diagnosis will be suggested, each one of them having its own confidence factor. When only one diagnosis is suggested ‘Carmsel’ asks for the clinical tests needed to c o n f i i it and gives a code number to use when the requested exploratory data becomes available. When several different diagnoses are proposed, ‘Canusel’ suggests the clinical tests needed to confirm each one of them and gives a mixed code number. At the end of this first stage a report with the name and history of the patient can be printed. The suggested diagnoses with their certainty factor as well as the requested clinical tests and code number are also printed. Confirmation diagnosis During the second stage ‘Canusel’ asks for the results of the clinical tests requested in the first stage and integrates them into the diagnostic model. At the beginning of this second consultation the code number previously assigned must be given to the system. If only one diagnosis was suggested, ‘Carrusel’ will confirm or reject it according to the results of the requested clinical tests. When several diagnoses have been proposed, ‘Carrusel’ will select the most likely according to the new information provided by the clinical examination. When one or more diagnoses are rejected ‘Canusel’ explains the reasons for the rejection. Therapeutic advice is given according to the selected diagnosis. The user does not have to perform every test requested by ‘Carrusel’ in order to come back to the second stage of the diagnosis-an expert system is no more than a help for the clinician who should never feel constrained by the system. When the unperformed test is not considered essential by ‘Canusel’ the diagnostic procedure remains unchanged. If the unperformed test is considered essential by the system, the second stage is finished without a diagnostic confirmation. In such instances ‘Canusel’ explains the reasons for its decision. If the information provided by the patient is not related either to vertigo or dizziness, or if central anomalies are detected in the clinical examination, ‘Carrusel’ automatically displays a third group of disorders. Detailed information about cranial nerves, oculomotor system and brainstem evoked responses is then requested. All this information is used for the topodiagnosis of the lesion within the central nervous system. A complete neurootological report is prepared at the end of the procedure. Clinical understanding of ‘Carrusel’ Any of the manifold questions asked by ‘Carrusel’ during the consultation has complementary information which can be retrieved by the system user by the touch of a function key. This action does not interrupt the consultation process which returns to its previous state when any key is pressed. This feature makes ‘Canusel’ a powerful educational tool for students and non-expert physicians. One of the most important clinical characteristics of ‘Canusel’ is that it never uses the words “vertigo” or “dizziness” during the data acquisition process. It is the system itself which deduces the main symptom from the information given by the patient. Currently, ‘Canusel’ has 155 double choice questions and 98 multiple choice questions with 315 options. The diagnosis is usually reached after only 20 to 40 questions.
BASIS FOR THE DATA PROCESSING The objective of ‘Canusel’ is focused on knowledge representation in order to allow the system to act in accordance with a predetermined strategy which is the diagnosis by the anamnesis (6). This approach requires a clinical diagnosis in which the patient history
163
164 C. Gavilan et al.
Acta Otolaryngol (Stockh) 110
C A K K U S E L HLOCKS LIIAGKAM MODULE
KNOWLEDGE DATA BASE Rules: -
Downloaded by [Australian National University] at 01:56 10 May 2016
Inference Rules Contexts Refining Rules
/
INFERENCE ENGINE
Make Questions Update Databases Verify certainties
FACTS D A ~ A S E Collected Data Inferred Data Aqenda:
Question Explanation Diagnostic Explanation Therapeutics Measures
General Strategy
Meta-rules: Temporary Strategies Meta-knowledges Heuristics
\
Activated Rules Standby Rules Partials Objectives Control Notes
DIAG NOSTIC MODULE Recover Subobjectives Delete Redundancies Select Clinical Tests Generate Reports Cleaning Facts D. Base
Fig. I
plays an essential role in order to select the exploratory tests indispensable to confirm the diagnosis. Since the system has to simulate the doctor’s behaviour, it is the system itself which, like the doctor, directs the interrogation. It does not accept the kind of information spontaneously given by the patient. This information is frequently unnecessary and, at other times, can even be counterproductive. ‘Canusel’ must make sure that the information entered is correct. When the system requests important information, it asks again in a different way to ascertain, by redundancy, the quality of the information provided by the patient. Because the questions have to be answered by the patient, they should be clear and devoid of technical terms. Since the system does not “see” the patient, sometimes the questions are directed at the user who is the “eyes” of the expert system providing it with such information as the clinician has from the direct observation of the patient. A second step was the delivery vehicle, the machine in which the system would run. Many reasons, including the price, pointed to using a PC. All subsequent development was therefore addressed in this direction: the development of a system able to offer high performance, both as to diagnostic quality and response time, at a low price.
‘Carrusel’:An expert system
Downloaded by [Australian National University] at 01:56 10 May 2016
Acta Otolaryngol (Stockh) 110
Initially, the system was developed along the lines of a pure “production system” like those proposed by Post in 1943. As the implementation progressed the need became apparent to modify it more towards the direction taken by Davis in TEIRESIAS (12). ‘Carmsel’ is thus composed of a “production system” with high level control by metarules, but introducing new strategies especially in the way of handling certainty factors in the proposed diagnoses. The system can be divided into five blocks (Fig. 1). 1. The Inference Engine controls the general strategy of the expert system and communicates with the rest of the modules. 2. The Knowledge Data Base is divided into three parts. The first two, rules and metarules, are memory resident. The third, texts, remains on disc to be consulted only when it is required by the system. 3. The Fact Data Base constitutes the dynamic memory, continuously maintaining an anamnesis store and a store of the results inferred by the system. 4. The Interrogation Module is in charge of questioning the patient, while avoiding repetitions, incongruities, etc. and recording the information in the facts data base. 5. When all goals have been satisfied the Diagnostic Module is triggered by the inference engine. It is in charge of the presentation of the final diagnoses, selection of clinical tests to be proposed, report creation, and preparation of the fact data base for a new consultation. CLINICAL USEFULNESS O F ‘CARRUSEL’ Before reporting the clinical results of ‘Carrusel’ two considerations are needed: 1. The goal of an expert system is to emulate the behavior of the “expert” that developed it. 2. Most vestibular disorders can only be diagnosed through a deductive process with no objective clinical confirmation. According to these conditions, the diagnostic skill of ‘Carrusel’ has been evaluated comparing its results with our own clinical diagnoses. This is the first step in the design of an expert system. The next step in the evolution of any expert system is the validation
Table 11. Results of ‘Carrusel’ according to con$dence factor Confidence factor
Agreement (%)
Disagreement (%)
>30% S 5 0 %
26 (84) 49 (100) 90 (100)
5 (16) 0
>SO% 70 %
0
Table 111. Number of questions asked by ‘Carrusel’ before diagnosis N s20 >20 L 4 0 >40 S60 >60
Vertigo (%) 9 (9) 92 (91) 0 0
Dizziness (%) 0 38 (55) 19 (27.5) 12 (17.5)
Total (%) 9 (5.3)
130 (76.5) 19 (11.2) 12 (7)
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process that must be performed by specialists who did not collaborate in the creation of the system. As was mentioned before, ‘Carrusel’ is now being validated by several international otoneurologists. For 16 months ‘Carrusel’ has been successively revised in order to increase its efficiency. Different studies have been performed to improve the diagnostic precision of the system. In the first evaluation, including 58 patients, the agreement between medical diagnosis and ‘Carmsel’ was 78%. The last study was performed on 170 patients and the agreement rate reached 97 %. Results To assess the clinical usefulness of ‘Carrusel’ a comparative study was performed on 170 consecutive patients studied at the section of otoneurology of our department. One hundred and nine patients had vertigo as the main symptom and 61 patients complained of dizziness. In 165 (97%+2) patients no clinical difference could be found between medical and computer diagnoses. Only in 5 (3 %+2) cases a contradictory diagnosis was obtained by ‘Canusel’. The diagnostic agreement was 97.2% for patients with vertigo and 96.7% for patients with dizziness. This difference was not statistically significant. The rate of correct diagnoses according to the confidence factor is shown in Table 11. For confidence factors over 50% the agreement between medical diagnosis and ‘Canusel’ was 100%. Five (16%) incorrect diagnoses were proposed by the expert system when the confidence factor ranged between 30% and 50%. However, only 31 (18.2%) patients were diagnosed by ‘Canusel’ with certainty factors lower than 50%. The number of questions asked by ‘Carrusel’ before reaching a diagnosis is shown in Table 111. While patients with vertigo were always diagnosed with less than 40 questions, patients with dizziness required a higher number of questions before a correct diagnosis was obtained. According to the total number of questions contained within ‘Canusel’, the average diagnostic process only uses 12% of the system’s capacity.
CONCLUSIONS At present, ‘Canusel’ has successfully completed its first phase. The system achieves a 97% rate of correct diagnoses when compared to our clinical diagnoses. As stated before, this is the first goal of any expert system. The next step, presently being undertaken, is the validation of the system by specialists not involved in its construction. According to the deductive nature of the diagnostic approach in otoneurology, differences among several specialists are to be expected. Close cooperation will be needed in order to achieve the best results from the system. REFERENCES 1. Alty JL, Cooms MJ. Sistemas expertos. Conceptos y ejemplos. Madrid: Diaz de Santos, 1986. 2. Simons GL. Introduccion a la inteligencia artificiai. Madrid: Diaz de Santos, 1987. 3. Wulfman CE. Sistemas expertos aplicados a la medicina: Oncocin. In: Rank Xerox, ed. Jornadas sobre Sistemas Expertos. Madrid: Egraf, 1987. 4. GavilAn C, Gallego J, GavilAn J. “Carrusel”: sistema experto en patologia vestibular. Actas del I Congreso Ibero-American0 de Medicina Interna y XVIII de la SEMI. Madrid: A&, 1988. 5 . Mira E, Schmid R, Zanocco P, Buizza A, Magenes G, Manfrin M. A computer-based consultation system (expert system) for the classification and diagnosis of dizziness. Adv OtoRhinoLaryngol 1988; 42: 77-80. 6. Gavilan C. Nuestra clasificacion de 10s sindromes vestibulares. Acta ORL Ibero-Amer 1972; 23:
818-30.
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'Carrusel': An expert system
7. Gavilan C , Gavilan J . Manejo del paciente con supuesta patologia vestibular. ORL Dips 1987; 14: 177-204. 8. Lindsay JR. The significance of a positional nystagmus in otoneurological diagnosis. Laryngoscope 1945; 55: 527-551. 9. Lindsay JR. Postural vertigo and positional nystagrnus. Ann Otol Rhinol Laryngol 1951; 60: 1134-56. 10. Aschan G , Stahle J . Vestibular neuritis. A nystagmographical study. J Laryngol 1956; 70: 497-5 1 1. 11. Dix MR, Hallpike CS. The pathology, symptomatology and diagnosis of certain common disorders of the vestibular system. Ann Otol Rhinol Laryngol 1952; 61: 987-1016. 12. Davis R. Meta-rules: reasoning about control. Artificial intelligence 1980; 15: 179-229.
Manuscript received December 15, 1989; accepted March 9, 1990
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Address for correspondence: Javier GavilBn, Servicio de ORL, Hospital La Paz, Pa Castellana, 261, 28046 Madrid, Spain
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ISSN: 0001-6489 (Print) 1651-2251 (Online) Journal homepage: http://www.tandfonline.com/loi/ioto20
‘Carnisel’: An Expert System for Vestibular Diagnosis CÉSar Gavilán, JosÉ Gallego & Javier Gavilán To cite this article: CÉSar Gavilán, JosÉ Gallego & Javier Gavilán (1990) ‘Carnisel’: An Expert System for Vestibular Diagnosis, Acta Oto-Laryngologica, 110:3-4, 161-167, DOI: 10.3109/00016489009122532 To link to this article: http://dx.doi.org/10.3109/00016489009122532
Published online: 08 Jul 2009.
Submit your article to this journal
Article views: 11
View related articles
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=ioto20 Download by: [Australian National University]
Date: 10 May 2016, At: 01:56
Acta Otolaryngol (Stockh) 1990; 110: 161-167
‘Carrusel’: An Expert System for Vestibular Diagnosis CESAR GAVILAN,’ JOSE GALLEGO* and JAVIER GAVILAN’ From the ‘Department of Otorhinolaryngology, Ln Par Hospitnl, Autonomous University, and the *System Diuision of Rank Xerox, Madrid, Spnin
Downloaded by [Australian National University] at 01:56 10 May 2016
Gavilan C, Gallego J, Gavilan J. ‘Carrusel’: An expert system for vestibular diagnosis. Acta Otolaryngol (Stockh) 1990; 110: 161-167. Neuro-otology is the only field of otolaryngology in which diagnoses are mainly deductive. Although many technological advances have indeed occurred, the patient history still remains the most important part of the evaluation of a patient complaining of vertigo and/or dizziness. The use of artificial intelligence methods as an aid for the solution of clinical problems is not new. “Carrusel” is a Prolog build-up expert system dealing with the diagnosis of vestibular disorders that achieves a success rate of 97 % when compared to the human experts involved in its design. Key words: artiJcinl intelligence, expert system, vestibular diagnosis, neuro-otology .
INTRODUCTION Otorhinolaryngology is a specialty in which diagnoses are mainly based on visual methods and techniques and radiological, anatomical or pathological confirmation is frequent. However, accurate diagnoses of vestibular disorders, unlike the rest of ENT diagnoses, are based on deductive clinical data. It is seldom possible to reach clinical confirmation by means of complementary exploratory techniques, and the diagnostic doubt is always present. Here artificial intelligence has a special application. Artificial intelligence has already been applied to other fields in medicine in the form of expert systems (1-5). Vestibular disorders, because of their complexity and lack of diagnostic confirmation, may also profit from an expert, or knowledge-based system. In 1987 we began to create such an expert system, ‘Carrusel’. One year later we presented it at an international meeting and an overview of the system was published (4). Simultaneously, another expert system for the classification and diagnosis of dizziness was reported by another group (5). ‘Carrusel’ is now being validated by several international otoneurologists who were not involved in its creation. DIAGNOSTIC APPROACH ‘Carrusel’ is an expert system written in Prolog which runs in 640 Kb PC compatible computers. It assists in the diagnosis of all diseases related both to vertigo and dizziness, either peripheral or central and affecting the vestibular and/or the oculomotor system. Two independent stages integrate the diagnostic procedure. During the first-presumption diagnosis-‘Carrusel’ suggests the most probable diagnoses on the basis of the data provided by the patient history. The clinical tests needed to confirm these diagnoses are requested by the own expert system. A clinical history number is generated at the end of this stage to be used when the requested exploratory clinical data becomes available. During the second stage-confirmation diagnosis-‘Carrusel’ asks for the results of the clinical tests requested in the first stage and integrates them into the diagnostic model. Presumption diagnosis
The first diagnostic approach gives one or more suspected diagnoses deduced from the patient history data collected through a computer-controlled questionnaire. The reasoning 11-908405
Downloaded by [Australian National University] at 01:56 10 May 2016
162 C . Gavilrin et al.
Acta Otolaryngol (Stockh) 110
process is based on the diagnosis by medical history previously reported (6, 7). According to this method three different groups are considered under the names of vertigo, dizziness and dysequilibrium. The first group includes all diseases having true vertigo as a major symptom. The second group contains the disorders characterized by a vague non-specific feeling called dizziness. The latter group includes central disorders in which dysequilibrium without vertigo is the main symptom. This third group is not comprised in the presumption diagnostic process because the diagnosis of the disorders of the central nervous system is mainly based on data provided by the neurootological examination rather than from the patient history. Central disorders are included in the second stageconfirmation diagnosis-when the results of the clinical tests are requested to confirm the proposed diagnosis. For the first two groups ‘Carrusel’ tries to find a clinical diagnosis. For the central group it only attempts to get a topographic localization of the lesion, without considering the clinical name of the disease for the most part of the cases. The part dealing with diseases related to vertigo and dizziness without central nervous system participation has already been debugged and tested. In this section there are 36 different possible diagnoses (Table I) which access more than 2 300 rules or meta-rules. Two different kinds of rules are present within ‘Carrusel’: discriminant rules and confirming rules. Discriminant rules contain mandatory symptoms unavoidable for a given diagnosis. Confirming rules substantiate or unsubstantiate the presumption diagnosis and therefore are not unavoidable. In relation to the specific weight of the selected rules, all the diagnoses proposed during the first round are ordered from greater to lesser probability according to a confidence factor. Even though the number of rules to diagnose ratio is very high, sometimes the symptoms provided by the patient in his first visit may not fit any of the rules. This may be due either to the atypical appearance of the disease, to its short evolution, or to the lack of the patient’s cooperation when exposing his or her symptoms. If ‘Carrusel’ is not able to find any diagnosis in the first round, it will automatically begin a second loop in which the discriminant rules are very few and the confirming rules many. Since the importance of the certainty factor mainly depends on the discriminant rules, the confidence factor of the diagnoses obtained by this process will be lower than the one obtained in the first loop.
Table I . Clinical diagnoses as considered by ‘Carrusel’ Meniere’s syndrome Pseudo-Meniere’s syndrome (8. 9) Sudden vestibular loss (10) Sudden cochleo-vestibular loss Vestibular neuronitis (1 I ) Positional paroxysmal vertigo Labyrinthine fracture Serous labyrinthitis Suppurative labyrinthitis Labyrinthine fistula Perilymphatic fistula Central nuclear syndrome Otosclerosis with vertigo Ototoxic syndrome Single unidentifiable bout Atypical vestibular episodes Subclavian steal syndrome Postconcussion positional vertigo
Postconcussion syndrome Psychogenic syndrome Acrophobia Agoraphobia Motion sickness Malinger Diffuse cerebral ischemia Syncope Peripheral idiopathic degeneration Vertebrobasilar insufficiency Iatrogenic (surgical) trauma Cerebello pontine angle syndrome Orthostatic syndrome Cervical syndrome Cogan’s syndrome Drop attack VertigoiHeadache VIIIth nerve syndrome
Acta Otolaryngol (Stockh) 1 I0
‘Carrusel’:An expert system
Downloaded by [Australian National University] at 01:56 10 May 2016
When the symptoms provided by the patient are not very typical more than one diagnosis will be suggested, each one of them having its own confidence factor. When only one diagnosis is suggested ‘Carmsel’ asks for the clinical tests needed to c o n f i i it and gives a code number to use when the requested exploratory data becomes available. When several different diagnoses are proposed, ‘Canusel’ suggests the clinical tests needed to confirm each one of them and gives a mixed code number. At the end of this first stage a report with the name and history of the patient can be printed. The suggested diagnoses with their certainty factor as well as the requested clinical tests and code number are also printed. Confirmation diagnosis During the second stage ‘Canusel’ asks for the results of the clinical tests requested in the first stage and integrates them into the diagnostic model. At the beginning of this second consultation the code number previously assigned must be given to the system. If only one diagnosis was suggested, ‘Carrusel’ will confirm or reject it according to the results of the requested clinical tests. When several diagnoses have been proposed, ‘Carrusel’ will select the most likely according to the new information provided by the clinical examination. When one or more diagnoses are rejected ‘Canusel’ explains the reasons for the rejection. Therapeutic advice is given according to the selected diagnosis. The user does not have to perform every test requested by ‘Carrusel’ in order to come back to the second stage of the diagnosis-an expert system is no more than a help for the clinician who should never feel constrained by the system. When the unperformed test is not considered essential by ‘Canusel’ the diagnostic procedure remains unchanged. If the unperformed test is considered essential by the system, the second stage is finished without a diagnostic confirmation. In such instances ‘Canusel’ explains the reasons for its decision. If the information provided by the patient is not related either to vertigo or dizziness, or if central anomalies are detected in the clinical examination, ‘Carrusel’ automatically displays a third group of disorders. Detailed information about cranial nerves, oculomotor system and brainstem evoked responses is then requested. All this information is used for the topodiagnosis of the lesion within the central nervous system. A complete neurootological report is prepared at the end of the procedure. Clinical understanding of ‘Carrusel’ Any of the manifold questions asked by ‘Carrusel’ during the consultation has complementary information which can be retrieved by the system user by the touch of a function key. This action does not interrupt the consultation process which returns to its previous state when any key is pressed. This feature makes ‘Canusel’ a powerful educational tool for students and non-expert physicians. One of the most important clinical characteristics of ‘Canusel’ is that it never uses the words “vertigo” or “dizziness” during the data acquisition process. It is the system itself which deduces the main symptom from the information given by the patient. Currently, ‘Canusel’ has 155 double choice questions and 98 multiple choice questions with 315 options. The diagnosis is usually reached after only 20 to 40 questions.
BASIS FOR THE DATA PROCESSING The objective of ‘Canusel’ is focused on knowledge representation in order to allow the system to act in accordance with a predetermined strategy which is the diagnosis by the anamnesis (6). This approach requires a clinical diagnosis in which the patient history
163
164 C. Gavilan et al.
Acta Otolaryngol (Stockh) 110
C A K K U S E L HLOCKS LIIAGKAM MODULE
KNOWLEDGE DATA BASE Rules: -
Downloaded by [Australian National University] at 01:56 10 May 2016
Inference Rules Contexts Refining Rules
/
INFERENCE ENGINE
Make Questions Update Databases Verify certainties
FACTS D A ~ A S E Collected Data Inferred Data Aqenda:
Question Explanation Diagnostic Explanation Therapeutics Measures
General Strategy
Meta-rules: Temporary Strategies Meta-knowledges Heuristics
\
Activated Rules Standby Rules Partials Objectives Control Notes
DIAG NOSTIC MODULE Recover Subobjectives Delete Redundancies Select Clinical Tests Generate Reports Cleaning Facts D. Base
Fig. I
plays an essential role in order to select the exploratory tests indispensable to confirm the diagnosis. Since the system has to simulate the doctor’s behaviour, it is the system itself which, like the doctor, directs the interrogation. It does not accept the kind of information spontaneously given by the patient. This information is frequently unnecessary and, at other times, can even be counterproductive. ‘Canusel’ must make sure that the information entered is correct. When the system requests important information, it asks again in a different way to ascertain, by redundancy, the quality of the information provided by the patient. Because the questions have to be answered by the patient, they should be clear and devoid of technical terms. Since the system does not “see” the patient, sometimes the questions are directed at the user who is the “eyes” of the expert system providing it with such information as the clinician has from the direct observation of the patient. A second step was the delivery vehicle, the machine in which the system would run. Many reasons, including the price, pointed to using a PC. All subsequent development was therefore addressed in this direction: the development of a system able to offer high performance, both as to diagnostic quality and response time, at a low price.
‘Carrusel’:An expert system
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Acta Otolaryngol (Stockh) 110
Initially, the system was developed along the lines of a pure “production system” like those proposed by Post in 1943. As the implementation progressed the need became apparent to modify it more towards the direction taken by Davis in TEIRESIAS (12). ‘Carmsel’ is thus composed of a “production system” with high level control by metarules, but introducing new strategies especially in the way of handling certainty factors in the proposed diagnoses. The system can be divided into five blocks (Fig. 1). 1. The Inference Engine controls the general strategy of the expert system and communicates with the rest of the modules. 2. The Knowledge Data Base is divided into three parts. The first two, rules and metarules, are memory resident. The third, texts, remains on disc to be consulted only when it is required by the system. 3. The Fact Data Base constitutes the dynamic memory, continuously maintaining an anamnesis store and a store of the results inferred by the system. 4. The Interrogation Module is in charge of questioning the patient, while avoiding repetitions, incongruities, etc. and recording the information in the facts data base. 5. When all goals have been satisfied the Diagnostic Module is triggered by the inference engine. It is in charge of the presentation of the final diagnoses, selection of clinical tests to be proposed, report creation, and preparation of the fact data base for a new consultation. CLINICAL USEFULNESS O F ‘CARRUSEL’ Before reporting the clinical results of ‘Carrusel’ two considerations are needed: 1. The goal of an expert system is to emulate the behavior of the “expert” that developed it. 2. Most vestibular disorders can only be diagnosed through a deductive process with no objective clinical confirmation. According to these conditions, the diagnostic skill of ‘Carrusel’ has been evaluated comparing its results with our own clinical diagnoses. This is the first step in the design of an expert system. The next step in the evolution of any expert system is the validation
Table 11. Results of ‘Carrusel’ according to con$dence factor Confidence factor
Agreement (%)
Disagreement (%)
>30% S 5 0 %
26 (84) 49 (100) 90 (100)
5 (16) 0
>SO% 70 %
0
Table 111. Number of questions asked by ‘Carrusel’ before diagnosis N s20 >20 L 4 0 >40 S60 >60
Vertigo (%) 9 (9) 92 (91) 0 0
Dizziness (%) 0 38 (55) 19 (27.5) 12 (17.5)
Total (%) 9 (5.3)
130 (76.5) 19 (11.2) 12 (7)
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process that must be performed by specialists who did not collaborate in the creation of the system. As was mentioned before, ‘Carrusel’ is now being validated by several international otoneurologists. For 16 months ‘Carrusel’ has been successively revised in order to increase its efficiency. Different studies have been performed to improve the diagnostic precision of the system. In the first evaluation, including 58 patients, the agreement between medical diagnosis and ‘Carmsel’ was 78%. The last study was performed on 170 patients and the agreement rate reached 97 %. Results To assess the clinical usefulness of ‘Carrusel’ a comparative study was performed on 170 consecutive patients studied at the section of otoneurology of our department. One hundred and nine patients had vertigo as the main symptom and 61 patients complained of dizziness. In 165 (97%+2) patients no clinical difference could be found between medical and computer diagnoses. Only in 5 (3 %+2) cases a contradictory diagnosis was obtained by ‘Canusel’. The diagnostic agreement was 97.2% for patients with vertigo and 96.7% for patients with dizziness. This difference was not statistically significant. The rate of correct diagnoses according to the confidence factor is shown in Table 11. For confidence factors over 50% the agreement between medical diagnosis and ‘Canusel’ was 100%. Five (16%) incorrect diagnoses were proposed by the expert system when the confidence factor ranged between 30% and 50%. However, only 31 (18.2%) patients were diagnosed by ‘Canusel’ with certainty factors lower than 50%. The number of questions asked by ‘Carrusel’ before reaching a diagnosis is shown in Table 111. While patients with vertigo were always diagnosed with less than 40 questions, patients with dizziness required a higher number of questions before a correct diagnosis was obtained. According to the total number of questions contained within ‘Canusel’, the average diagnostic process only uses 12% of the system’s capacity.
CONCLUSIONS At present, ‘Canusel’ has successfully completed its first phase. The system achieves a 97% rate of correct diagnoses when compared to our clinical diagnoses. As stated before, this is the first goal of any expert system. The next step, presently being undertaken, is the validation of the system by specialists not involved in its construction. According to the deductive nature of the diagnostic approach in otoneurology, differences among several specialists are to be expected. Close cooperation will be needed in order to achieve the best results from the system. REFERENCES 1. Alty JL, Cooms MJ. Sistemas expertos. Conceptos y ejemplos. Madrid: Diaz de Santos, 1986. 2. Simons GL. Introduccion a la inteligencia artificiai. Madrid: Diaz de Santos, 1987. 3. Wulfman CE. Sistemas expertos aplicados a la medicina: Oncocin. In: Rank Xerox, ed. Jornadas sobre Sistemas Expertos. Madrid: Egraf, 1987. 4. GavilAn C, Gallego J, GavilAn J. “Carrusel”: sistema experto en patologia vestibular. Actas del I Congreso Ibero-American0 de Medicina Interna y XVIII de la SEMI. Madrid: A&, 1988. 5 . Mira E, Schmid R, Zanocco P, Buizza A, Magenes G, Manfrin M. A computer-based consultation system (expert system) for the classification and diagnosis of dizziness. Adv OtoRhinoLaryngol 1988; 42: 77-80. 6. Gavilan C. Nuestra clasificacion de 10s sindromes vestibulares. Acta ORL Ibero-Amer 1972; 23:
818-30.
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'Carrusel': An expert system
7. Gavilan C , Gavilan J . Manejo del paciente con supuesta patologia vestibular. ORL Dips 1987; 14: 177-204. 8. Lindsay JR. The significance of a positional nystagmus in otoneurological diagnosis. Laryngoscope 1945; 55: 527-551. 9. Lindsay JR. Postural vertigo and positional nystagrnus. Ann Otol Rhinol Laryngol 1951; 60: 1134-56. 10. Aschan G , Stahle J . Vestibular neuritis. A nystagmographical study. J Laryngol 1956; 70: 497-5 1 1. 11. Dix MR, Hallpike CS. The pathology, symptomatology and diagnosis of certain common disorders of the vestibular system. Ann Otol Rhinol Laryngol 1952; 61: 987-1016. 12. Davis R. Meta-rules: reasoning about control. Artificial intelligence 1980; 15: 179-229.
Manuscript received December 15, 1989; accepted March 9, 1990
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Address for correspondence: Javier GavilBn, Servicio de ORL, Hospital La Paz, Pa Castellana, 261, 28046 Madrid, Spain
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