Neurosurgical tactile discrimination training with haptic-based virtual reality simulation Achal Patel1, Nick Koshy1, Juan Ortega-Barnett1, Hoi C. Chan1, Yong-Fan Kuo2, Cristian Luciano3, Silvio Rizzi3, Martin Matulyauskas3, Patrick Kania3, Pat Banerjee3, Jaime Gasco1 1

Division of Neurosurgery, University of Texas Medical Branch, Galveston, USA, 2Division of Epidemiology and Biostatistics, Preventive Medicine Department, University of Texas Medical Branch, Galveston, USA, 3 Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, USA Objective: To determine if a computer-based simulation with haptic technology can help surgical trainees improve tactile discrimination using surgical instruments. Material and Methods: Twenty junior medical students participated in the study and were randomized into two groups. Subjects in Group A participated in virtual simulation training using the ImmersiveTouch simulator (ImmersiveTouch, Inc., Chicago, IL, USA) that required differentiating the firmness of virtual spheres using tactile and kinesthetic sensation via haptic technology. Subjects in Group B did not undergo any training. With their visual fields obscured, subjects in both groups were then evaluated on their ability to use the suction and bipolar instruments to find six elastothane objects with areas ranging from 1.5 to 3.5 cm2 embedded in a urethane foam brain cavity model while relying on tactile and kinesthetic sensation only. Results: A total of 73.3% of the subjects in Group A (simulation training) were able to find the brain cavity objects in comparison to 53.3% of the subjects in Group B (no training) (P 5 0.0183). There was a statistically significant difference in the total number of Group A subjects able to find smaller brain cavity objects (size # 2.5 cm2) compared to that in Group B (72.5 vs 40%, P 5 0.0032). On the other hand, no significant difference in the number of subjects able to detect larger objects (size § 3 cm2) was found between Groups A and B (75 vs 80%, P 5 0.7747). Conclusion: Virtual computer-based simulators with integrated haptic technology may improve tactile discrimination required for microsurgical technique. Keywords: Education, Haptic, Neurosurgery, Simulation, Training

Introduction Much practice and time are required to develop core microsurgical skills. The dogma of surgical training has revolved around the Halstedian model that has been paraphrased using the popular slogan ‘see one, do one, and teach one.’ This model of apprenticeship has been used to train surgeons around the world since the early 1900s. However, training surgeons in the modern era using this model may not always be ideal because of several factors such as work-hour restrictions for surgical residents, increasing operating room costs, limited resources, and increasing public awareness for patient safety. In light of this, accommodating bench work training to the traditional surgical training curriculum could help residents develop core surgical

Correspondence to: Jaime Gasco, Division of Neurological Surgery, University of Texas Medical Branch, 301, University Boulevard, Galveston, TX 77555-0517, USA. E-mail: [email protected]

ß W. S. Maney & Son Ltd 2014 DOI 10.1179/1743132814Y.0000000405

skills despite limited operative room exposure while reducing opportunity or financial costs for hospitals and attending faculty surgeons.1 Neurosurgical procedures can be additionally challenging to perform due to visual and spatial restraints. Many times precise movements are needed within surgical fields with high depth to working-area ratios, such as in the intracranial cavity, to avoid damage to nearby vital brain structures so compactly organized. As a result, microsurgical instruments are designed for working in small surgical fields that cannot accommodate a surgeon’s hands and therefore act as mediators between the tissue and our sensory system. In microsurgery, interpreting tactile and kinesthetic feedback via these surgical instruments becomes critical when visual feedback is limited or not enough by itself.2 Several types of simulation models have been used for benchwork training but are limited in their

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Figure 1 Brain cavity. (A) Bipolar and suction instruments are used to feel elastothane objects integrated within the urethane foam representing brain parenchymal tissue inside the brain cavity. (B) The elastothane objects varied in sizes from 1 to 3.5 cm2 (a 5 1.5 cm2; b 5 2.0 cm2; c 5 3 cm2; d 5 2.5 cm2; e 5 3.5 cm2; and f 5 1.0 cm2).

abilities to replicate the surgical environment. With technological advancement, computer-based virtual simulation has the potential to replicate the surgical environment and critical core surgical tasks with high fidelity. Haptic feedback is an important component of virtual simulation that combines tactile and kinesthetic feedback. To date, few studies have investigated virtual simulation with haptic feedback for surgical training. Although still in its early stages of development, we present a study on how computerbased virtual simulation with haptic feedback could be used to improve a core surgical skill that involves discriminating tactile and kinesthetic sensation.

Materials and Methods Subjects Twenty junior medical subjects voluntarily participated in this study and were randomized into two groups (10 subjects in each group). The subjects’ previous surgical experiences were not ascertained.

Materials The ImmersiveTouch (ImmersiveTouch, Inc., Chicago, IL, USA) virtual simulator is driven by an application running on a computer workstation. The simulator provides three-dimensional (3D), high-resolution graphics using a flat panel display along with tactile and

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kinesthetic feedback through two haptic devices (Phantom Omni, Sensable Technologies, Woburn, MA, USA), each used to manipulate a surgical instrument simultaneously. A stereoscopic sensor is attached to the 3D glasses worn by the subject and detects the subject’s head position in real-time to augment the viewing angle of the 3D graphics on the flat panel display. The synthetic brain cavity model consisted of urethane foam resembling brain tissue with embedded elastothane objects of various sizes ranging from 1 to 3.5 cm2 with 0.5 cm2 increments (Fig. 1, Medical Accessories and Research, Zeeland, MI, USA). The urethane surface cavity was porous and rough compared to the elastothane objects that had a smooth surface and rubbery consistency. The density of the porous urethane foam was 0.1–0.13 g/cm3, and that of the elastothane objects was 1.1 g/cm3. A surface analysis under 306 and 706 magnification using a photomicroscope (Wild Photomacroscope M400; Leica, Heerbrugg, Switzerland) and a field emission scanning electron microscope (Hitachi High Technologies Corporation, Tokyo, Japan) was done to compare the surface properties of the elastothane and urethane foam (Fig. 2).

Experiment Subjects in Group A did a simulation exercise prior to the task involving the brain cavity model. The objective of the simulation exercise was to detect the softest virtual sphere out of three spheres of different firmness by means of tactile and kinesthetic feedback via virtual instruments. Haptic devices are used to maneuver the virtual microsurgical instruments, a Malis bipolar (right haptic) and Frazier suction (left haptic). If the subject correctly detects the softest sphere by simultaneously pressing a button on the haptic device and wielding the bipolar or suction tool to maintain virtual contact with the sphere, then a distinct audible sound is produced and another set of spheres with less variation in firmness is presented to increase the level of difficulty. On the other hand, an incorrect selection is conveyed using vibration feedback from the haptic device along with a specific audible sound and the firmness of all three spheres are reset to different values without advancing the level of difficulty (Fig. 3). Unlimited attempts were allowed to choose the correct sphere for any of the 15 levels of difficulty. Subjects in Group B were excluded from the virtual simulation exercise. Subjects in both groups were then evaluated on detecting the six elastothane objects of varying sizes embedded in the brain cavity model by using bipolar and suction devices (Fig. 1). The visual properties of the brain cavity were obscured by requiring each subject to wear tinted glasses in a dim environment.

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Figure 2 Microscopic surface analysis comparison. Urethane foam brain cavity under 306 magnification (A) and 706 magnification (B). The foam is highly porous. Elastothane object surface under 306 magnification (C) and 706 magnification (D) with scanning electron microscope.

Subjects were allowed a maximum of 15 attempts for 10 minutes and would notify the proctor when he or she determined an object had been located using the aforementioned microsurgical instruments to feel the brain cavity. Redetection of the same object by the subject was not recorded.

Analysis The objects of each size detected by the subjects were recorded. Using a generalized linear mixed model with binomial distribution, the within-subject factor was not significant (intra-class correlation: 0.006, P 5 0.9350) and each object detection per subject was treated independently in a one-sided Fisher Exact test to compare the total number of subjects that detected each of the six objects at least once between both groups.

objects of 1.5 and 3.0 cm2. A general trend of difference in the number of subjects able to locate objects of 2.5 cm2 or lesser size can be appreciated between both groups (Fig. 4). However, the majority of subjects in both groups were able to detect objects of 3.0 cm2 or greater size. With these findings, a cutoff point in the size of the objects was determined, and there was a statistically significant difference in the total number of Group A subjects able to find smaller brain cavity objects (size # 2.5 cm2) compared to that in Group B (72.5 ¡ 21.9% vs 40.0 ¡ 21.1%, P 5 0.0032). On the other hand, no significant difference in the number of subjects able to detect larger objects (size § 3 cm2) was found between Groups A and B (75.0 ¡ 26.4% vs 80.0 ¡ 25.8%, P 5 0.7747).

Results

Discussion

All students in Group A completed the 15 levels of the simulation training. A total of 73.3 ¡ 14.1% of the subjects in Group A were able to identify the various brain cavity objects in comparison to only 53.3 ¡ 20.5% of the subjects in Group B, who were excluded from the simulation exercise (P 5 0.0183). More subjects in Group A were able to locate objects of each size than subjects in Group B except for

Various simulators have been used in neurosurgery to develop surgical skills.3 This study explored the potential of combining advanced 3D virtual simulation with haptics to help develop a basic operative skill that involves interpreting tactile and kinesthetic feedback using microsurgical instruments. Developing this skill is essential in surgery when navigating through the complex and varying human anatomy and trying

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Figure 3 Virtual simulation module. (A) Immersive touch virtual simulator. (B) Haptic articulating arm. (C) Bipolar and suction tools used to feel the virtual spheres. Tactile and kinesthetic feedback is generated via the haptic devices.

to distinguish normal from abnormal tissues that are visually inseparable. Overall, the object detection rate in the brain cavity was higher for Group A subjects (73.3%), who did the simulation exercise, than for Group B subjects (53.3%). For each of the objects 2.5 cm2 or less, a general trend of more detections in Group A than Group B was seen except for the 1.5 cm2 object.

Figure 4 Data analysis shows total number of students within each group able to locate different objects within the brain cavity. Group A did the simulation exercise while Group B did not.

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Performing the simulation exercise may have increased the sensitivity of the subjects and improved their tactile discrimination ability for locating objects using the microsurgical instruments. The outperformance of Group B subjects for the 1.5 cm2 object may have been incidental since we could not explain this finding. Detections of 3.0 and 3.5 cm2 objects were similar for both groups which may suggest a threshold size necessary to overcome the challenge of discriminating between the urethane foam and the elastothane objects using microsurgical instruments. The aforementioned physical properties of the urethane and elastothane materials were thought to be adequate for discrimination using tactile and kinesthetic sensory modalities based on the senior author’s surgical experience. Recreating tissue properties such as compliance is made possible by computer algorithms that control the haptic stylus to generate resistance. The haptic simulator’s capability of generating a large variation in resistance was thought to have adequate precision to mimic the difference between the tissue properties of urethane and elastothane materials based on the senior author’s expertise. Recently, a national survey indicated neurosurgery program directors showed a high regard for using computer-based simulation to train residents.4

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Computer-based simulators offer several advantages. Minimal maintenance and setup time are needed other than periodic software upgrades. This allows trainees to use the simulator at their convenience. Also, a variety of surgical tasks can be recreated with modifications to software but not hardware and may prove to be less costly over the long term in comparison to non-reusable physical models. Furthermore, tissue compliance, pressure, and pulsation are lost in cadaver models but could be recreated in virtual simulators.5 On the other hand, some limitations will need to be addressed. As this technology continues to unfold, extensive validation will need to be developed to ensure the effectiveness of computerbased simulation training. Multiple aspects of validation needing assessment include realism of the simulation, being able to assess surgical skills to differentiate between novice and expert surgeons, and its ability to predict performance in the operating room.6 Although the current hardware technology is fairly sophisticated, further innovation will help increase the realism of the simulation experience. In hindsight, this study is an introductory exploration of computer-based virtual simulation training. Further studies will be needed to expand upon the current study’s limitations. The number of subjects in this study limits its statistical power. Incorporating surgical residents from multiple training centers would provide better statistical power. Also, the benefit of simulation training could be assessed for residents of different training levels. Moreover, improvising the study design to look for difference in the performance of a surgical task before and after virtual simulation training may better elucidate its effectiveness.

Conclusions Virtual computer-based simulators with haptics could be useful for learning operative skills such as tactile discrimination, but further studies are needed to investigate its utility in surgical training.

Virtual reality tactile discrimination trainer

Disclaimer Statements Contributors Achal Patel, Nick Koshy, Thomas Holbrook, Juan Ortega-Barnett, Hoi Chan, and Jaime Gasco helped to write different portions of the manuscript. Yong Kuo did statistical analysis. Cristian Luciano, Silvio Rizzi, Martin Matulyauskas, Patrick Kania, and Pat Banerjee developed the simulation application and contributed to portions of the design of this study. Funding Provided by UTMB Neurosurgery. Conflicts of interest Coauthor Dr Banerjee owns stock in ImmersiveTouchH, Inc. Drs Luciano and Banerjee are part-time employees of ImmersiveTouch, Inc. The other authors have no personal financial or institutional interest in any of the drugs, materials, or devices described in this article. Ethics approval Ethical approval was not required.

Acknowledgements This work was supported in part by NIH NINDS grant 2R44NS066557. We thank Ms Julie Wen, MS and Ms Joy Grise, PA for the assistance in the surface analysis and photography.

References 1 Bridges M, Diamond DL. The financial impact of teaching surgical residents in the operating room. Am J Surg. 1999;177:28–32. 2 Hu R, Barner KE, Steiner KV. A generalized haptic feedback approach for arbitrarily shaped objects. Stud Health Technol Inform. 2011;163:224–30. 3 Malone HR, Syed ON, Downes MS, D’Ambrosio AL, Quest DO, Kaiser MG. Simulation in neurosurgery: a review of computer-based simulation environments and their surgical applications. Neurosurgery. 2010;67:1105–16. 4 Ganju A, Aoun SG, Daou MR, El Ahmadieh TY, Chang A, Wang L, et al. The role of simulation in neurosurgical education: a survey of 99 United States Neurosurgery Program Directors. World Neurosurg. 2012;80(5):e1–8. 5 Schostek S, Schurr MO, Buess GF. Review on aspects of artificial tactile feedback in laparoscopic surgery. Med Eng Phys. 2009;31:887–98. 6 Abboudi H, Khan MS, Aboumarzouk O, Guru KA, Challacombe B, Dasgupta P, et al. Current status of validation for robotic surgery simulators – a systematic review. BJU Int. 2013;111:194–205.

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Neurosurgical tactile discrimination training with haptic-based virtual reality simulation.

To determine if a computer-based simulation with haptic technology can help surgical trainees improve tactile discrimination using surgical instrument...
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