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Boyajian GP, Zulbaran-Rojas A, Najafi B, Atique MMU, Loor G, Gilani R, Schutz A, Wall MJ, Coselli JS, Moon MR, Rosengart TK, Ghanta RK. Development of a Sensor Technology to Objectively Measure Dexterity for Cardiac Surgical Proficiency. Ann Thorac Surg 2024; 117:635-643. [PMID: 37517533 DOI: 10.1016/j.athoracsur.2023.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/04/2023] [Accepted: 07/11/2023] [Indexed: 08/01/2023]
Abstract
BACKGROUND Technical skill is essential for good outcomes in cardiac surgery. However, no objective methods exist to measure dexterity while performing surgery. The purpose of this study was to validate sensor-based hand motion analysis (HMA) of technical dexterity while performing a graft anastomosis within a validated simulator. METHODS Surgeons at various training levels performed an anastomosis while wearing flexible sensors (BioStamp nPoint, MC10 Inc) with integrated accelerometers and gyroscopes on each hand to quantify HMA kinematics. Groups were stratified as experts (n = 8) or novices (n = 18). The quality of the completed anastomosis was scored using the 10 Point Microsurgical Anastomosis Rating Scale (MARS10). HMA parameters were compared between groups and correlated with quality. Logistic regression was used to develop a predictive model from HMA parameters to distinguish experts from novices. RESULTS Experts were faster (11 ± 6 minutes vs 21 ± 9 minutes; P = .012) and used fewer movements in both dominant (340 ± 166 moves vs 699 ± 284 moves; P = .003) and nondominant (359 ± 188 moves vs 567 ± 201 moves; P = .02) hands compared with novices. Experts' anastomoses were of higher quality compared with novices (9.0 ± 1.2 MARS10 vs 4.9 ± 3.2 MARS10; P = .002). Higher anastomosis quality correlated with 9 of 10 HMA parameters, including fewer and shorter movements of both hands (dominant, r = -0.65, r = -0.46; nondominant, r = -0.58, r = -0.39, respectively). CONCLUSIONS Sensor-based HMA can distinguish technical dexterity differences between experts and novices, and correlates with quality. Objective quantification of hand dexterity may be a valuable adjunct to training and education in cardiac surgery training programs.
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Affiliation(s)
- Gregory P Boyajian
- Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Alejandro Zulbaran-Rojas
- Division of Vascular Surgery and Endovascular Therapy, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Bijan Najafi
- Division of Vascular Surgery and Endovascular Therapy, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Md Moin Uddin Atique
- Division of Vascular Surgery and Endovascular Therapy, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Gabriel Loor
- Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Ramyar Gilani
- Division of Vascular Surgery and Endovascular Therapy, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Alexander Schutz
- Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Matthew J Wall
- Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Joseph S Coselli
- Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Marc R Moon
- Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Todd K Rosengart
- Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Ravi K Ghanta
- Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas.
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Singh R, Godiyal AK, Chavakula P, Suri A. Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment. Bioengineering (Basel) 2023; 10:bioengineering10040465. [PMID: 37106652 PMCID: PMC10136274 DOI: 10.3390/bioengineering10040465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 02/24/2023] [Accepted: 02/26/2023] [Indexed: 04/29/2023] Open
Abstract
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.
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Affiliation(s)
- Ramandeep Singh
- Neuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Anoop Kant Godiyal
- Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Parikshith Chavakula
- Neuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Ashish Suri
- Neuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110029, India
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Koskinen J, Huotarinen A, Elomaa AP, Zheng B, Bednarik R. Movement-level process modeling of microsurgical bimanual and unimanual tasks. Int J Comput Assist Radiol Surg 2021; 17:305-314. [PMID: 34913139 PMCID: PMC8784365 DOI: 10.1007/s11548-021-02537-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 11/19/2021] [Indexed: 11/09/2022]
Abstract
Purpose Microsurgical techniques require highly skilled manual handling of specialized surgical instruments. Surgical process models are central for objective evaluation of these skills, enabling data-driven solutions that can improve intraoperative efficiency. Method We built a surgical process model, defined at movement level in terms of elementary surgical actions (\documentclass[12pt]{minimal}
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\begin{document}$$n=4$$\end{document}n=4) and targets (\documentclass[12pt]{minimal}
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\begin{document}$$n=4$$\end{document}n=4). The model also included nonproductive movements, which enabled us to evaluate suturing efficiency and bi-manual dexterity. The elementary activities were used to investigate differences between novice (\documentclass[12pt]{minimal}
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\begin{document}$$n=5$$\end{document}n=5) and expert surgeons (\documentclass[12pt]{minimal}
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\begin{document}$$n=5$$\end{document}n=5) by comparing the cosine similarity of vector representations of a microsurgical suturing training task and its different segments. Results Based on our model, the experts were significantly more efficient than the novices at using their tools individually and simultaneously. At suture level, the experts were significantly more efficient at using their left hand tool, but the differences were not significant for the right hand tool. At the level of individual suture segments, the experts had on average 21.0 % higher suturing efficiency and 48.2 % higher bi-manual efficiency, and the results varied between segments. Similarity of the manual actions showed that expert and novice surgeons could be distinguished by their movement patterns. Conclusions The surgical process model allowed us to identify differences between novices’ and experts’ movements and to evaluate their uni- and bi-manual tool use efficiency. Analyzing surgical tasks in this manner could be used to evaluate surgical skill and help surgical trainees detect problems in their performance computationally.
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Affiliation(s)
- Jani Koskinen
- School of Computing, University of Eastern Finland, 80110, Joensuu, Finland.
| | - Antti Huotarinen
- Department of Neurosurgery, Institute of Clinical Medicine, Kuopio University Hospital, 70211, Kuopio, Finland
- Microsurgery Center, Kuopio University Hospital, 70211, Kuopio, Finland
| | - Antti-Pekka Elomaa
- Department of Neurosurgery, Institute of Clinical Medicine, Kuopio University Hospital, 70211, Kuopio, Finland
- Microsurgery Center, Kuopio University Hospital, 70211, Kuopio, Finland
| | - Bin Zheng
- Surgical Simulation Research Lab, Department of Surgery, University of Alberta, Edmonton, AB, Canada
| | - Roman Bednarik
- School of Computing, University of Eastern Finland, 80110, Joensuu, Finland
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