Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment
Craniotomy is a fundamental component of neurosurgery that involves the removal of the skull bone flap. Simulation-based training of craniotomy is an efficient method to develop competent skills outside the operating room. Traditionally, an expert surgeon evaluates the surgical skills using rating s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/4/465 |
| _version_ | 1797606379230330880 |
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| author | Ramandeep Singh Anoop Kant Godiyal Parikshith Chavakula Ashish Suri |
| author_facet | Ramandeep Singh Anoop Kant Godiyal Parikshith Chavakula Ashish Suri |
| author_sort | Ramandeep Singh |
| collection | DOAJ |
| description | Craniotomy is a fundamental component of neurosurgery that involves the removal of the skull bone flap. Simulation-based training of craniotomy is an efficient method to develop competent skills outside the operating room. Traditionally, an expert surgeon evaluates the surgical skills using rating scales, but this method is subjective, time-consuming, and tedious. Accordingly, the objective of the present study was to develop an anatomically accurate craniotomy simulator with realistic haptic feedback and objective evaluation of surgical skills. A CT scan segmentation-based craniotomy simulator with two bone flaps for drilling task was developed using 3D printed bone matrix material. Force myography (FMG) and machine learning were used to automatically evaluate the surgical skills. Twenty-two neurosurgeons participated in this study, including novices (n = 8), intermediates (n = 8), and experts (n = 6), and they performed the defined drilling experiments. They provided feedback on the effectiveness of the simulator using a Likert scale questionnaire on a scale ranging from 1 to 10. The data acquired from the FMG band was used to classify the surgical expertise into novice, intermediate and expert categories. The study employed naïve Bayes, linear discriminant (LDA), support vector machine (SVM), and decision tree (DT) classifiers with leave one out cross-validation. The neurosurgeons’ feedback indicates that the developed simulator was found to be an effective tool to hone drilling skills. In addition, the bone matrix material provided good value in terms of haptic feedback (average score 7.1). For FMG-data-based skills evaluation, we achieved maximum accuracy using the naïve Bayes classifier (90.0 ± 14.8%). DT had a classification accuracy of 86.22 ± 20.8%, LDA had an accuracy of 81.9 ± 23.6%, and SVM had an accuracy of 76.7 ± 32.9%. The findings of this study indicate that materials with comparable biomechanical properties to those of real tissues are more effective for surgical simulation. In addition, force myography and machine learning provide objective and automated assessment of surgical drilling skills. |
| first_indexed | 2024-03-11T05:14:22Z |
| format | Article |
| id | doaj.art-c449f2555fc84e3ebbf6b166f2993475 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T05:14:22Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-c449f2555fc84e3ebbf6b166f29934752023-11-17T18:22:28ZengMDPI AGBioengineering2306-53542023-04-0110446510.3390/bioengineering10040465Craniotomy Simulator with Force Myography and Machine Learning-Based Skills AssessmentRamandeep Singh0Anoop Kant Godiyal1Parikshith Chavakula2Ashish Suri3Neuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110029, IndiaDepartment of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, New Delhi 110029, IndiaNeuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110029, IndiaNeuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110029, IndiaCraniotomy is a fundamental component of neurosurgery that involves the removal of the skull bone flap. Simulation-based training of craniotomy is an efficient method to develop competent skills outside the operating room. Traditionally, an expert surgeon evaluates the surgical skills using rating scales, but this method is subjective, time-consuming, and tedious. Accordingly, the objective of the present study was to develop an anatomically accurate craniotomy simulator with realistic haptic feedback and objective evaluation of surgical skills. A CT scan segmentation-based craniotomy simulator with two bone flaps for drilling task was developed using 3D printed bone matrix material. Force myography (FMG) and machine learning were used to automatically evaluate the surgical skills. Twenty-two neurosurgeons participated in this study, including novices (n = 8), intermediates (n = 8), and experts (n = 6), and they performed the defined drilling experiments. They provided feedback on the effectiveness of the simulator using a Likert scale questionnaire on a scale ranging from 1 to 10. The data acquired from the FMG band was used to classify the surgical expertise into novice, intermediate and expert categories. The study employed naïve Bayes, linear discriminant (LDA), support vector machine (SVM), and decision tree (DT) classifiers with leave one out cross-validation. The neurosurgeons’ feedback indicates that the developed simulator was found to be an effective tool to hone drilling skills. In addition, the bone matrix material provided good value in terms of haptic feedback (average score 7.1). For FMG-data-based skills evaluation, we achieved maximum accuracy using the naïve Bayes classifier (90.0 ± 14.8%). DT had a classification accuracy of 86.22 ± 20.8%, LDA had an accuracy of 81.9 ± 23.6%, and SVM had an accuracy of 76.7 ± 32.9%. The findings of this study indicate that materials with comparable biomechanical properties to those of real tissues are more effective for surgical simulation. In addition, force myography and machine learning provide objective and automated assessment of surgical drilling skills.https://www.mdpi.com/2306-5354/10/4/465surgical skillsdrillingbone matrix3D printingforce myographyartificial intelligence |
| spellingShingle | Ramandeep Singh Anoop Kant Godiyal Parikshith Chavakula Ashish Suri Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment Bioengineering surgical skills drilling bone matrix 3D printing force myography artificial intelligence |
| title | Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment |
| title_full | Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment |
| title_fullStr | Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment |
| title_full_unstemmed | Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment |
| title_short | Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment |
| title_sort | craniotomy simulator with force myography and machine learning based skills assessment |
| topic | surgical skills drilling bone matrix 3D printing force myography artificial intelligence |
| url | https://www.mdpi.com/2306-5354/10/4/465 |
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