دورية أكاديمية
Multi-Modal Deep Learning for Assessing Surgeon Technical Skill.
| العنوان: | Multi-Modal Deep Learning for Assessing Surgeon Technical Skill. |
|---|---|
| المؤلفون: | Kasa K; Orthopaedic Biomechanics Lab, Holland Bone and Joint Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada., Burns D; Orthopaedic Biomechanics Lab, Holland Bone and Joint Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.; Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada.; Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada., Goldenberg MG; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada., Selim O; Department of Surgery, Royal Victoria Regional Health Center, Barrie, ON L4M 6M2, Canada., Whyne C; Orthopaedic Biomechanics Lab, Holland Bone and Joint Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.; Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada.; Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada., Hardisty M; Orthopaedic Biomechanics Lab, Holland Bone and Joint Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.; Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada. |
| المصدر: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Sep 27; Vol. 22 (19). Date of Electronic Publication: 2022 Sep 27. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Basel, Switzerland : MDPI, c2000- |
| مواضيع طبية MeSH: | Deep Learning* , Surgeons*, Algorithms ; Clinical Competence ; Humans ; Suture Techniques/education |
| مستخلص: | This paper introduces a new dataset of a surgical knot-tying task, and a multi-modal deep learning model that achieves comparable performance to expert human raters on this skill assessment task. Seventy-two surgical trainees and faculty were recruited for the knot-tying task, and were recorded using video, kinematic, and image data. Three expert human raters conducted the skills assessment using the Objective Structured Assessment of Technical Skill (OSATS) Global Rating Scale (GRS). We also designed and developed three deep learning models: a ResNet-based image model, a ResNet-LSTM kinematic model, and a multi-modal model leveraging the image and time-series kinematic data. All three models demonstrate performance comparable to the expert human raters on most GRS domains. The multi-modal model demonstrates the best overall performance, as measured using the mean squared error (MSE) and intraclass correlation coefficient (ICC). This work is significant since it demonstrates that multi-modal deep learning has the potential to replicate human raters on a challenging human-performed knot-tying task. The study demonstrates an algorithm with state-of-the-art performance in surgical skill assessment. As objective assessment of technical skill continues to be a growing, but resource-heavy, element of surgical education, this study is an important step towards automated surgical skill assessment, ultimately leading to reduced burden on training faculty and institutes. |
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| فهرسة مساهمة: | Keywords: biomedical engineering; computer vision; deep learning; human activity recognition; machine learning; multi-modal; surgical education; surgical skills assessment |
| تواريخ الأحداث: | Date Created: 20221014 Date Completed: 20221017 Latest Revision: 20221019 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC9571767 |
| DOI: | 10.3390/s22197328 |
| PMID: | 36236424 |
| قاعدة البيانات: | MEDLINE |
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