Automated objective surgical skill assessment in the operating room from unstructured tool motion in septoplasty
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Previous work on surgical skill assessment using intraoperative tool motion has focused on highly structured surgical tasks such as cholecystectomy and used generic motion metrics such as time and number of movements. Other statistical methods such as hidden Markov models (HMM) and descriptive curve coding (DCC) have been successfully used to assess skill in structured activities on bench-top tasks. Methods to assess skill and provide effective feedback to trainees for unstructured surgical tasks in the operating room, such as tissue dissection in septoplasty, have yet to be developed.
We proposed a method that provides a descriptive structure for septoplasty by automatically segmenting it into higher-level meaningful activities called strokes. These activities characterize the surgeon’s tool motion pattern. We constructed a spatial graph from the sequence of strokes in each procedure and used its properties to train a classifier to distinguish between expert and novice surgeons. We compared the results from our method with those from HMM, DCC, and generic metric-based approaches.
We showed that our method—with an average accuracy of 91 %—performs better or equal than these state-of-the-art methods, while simultaneously providing surgeons with an intuitive understanding of the procedure.
In this study, we developed and evaluated an automated approach to objectively assess surgical skill during unstructured task of tissue dissection in nasal septoplasty.
KeywordsUnstructured activities Partially observed time series Surgical skill assessment Feature extraction Septoplasty Feedback
We gratefully acknowledge support from NIH 5R21DE022656-02. We would also like to thank participating attending surgeons—Drs. Kofi Boahene, Patrick Byrne, Ira Papel, and Theda Kontis, and trainee surgeons—Drs. Sun Ahn, Amit Kocchar, Linda Lee, Ryan Li, Myriam Loyo, Sofia Lyford-Pike, Peter Revenaugh, David Smith, Babar Sultan, David Mener, Samuel Oyer, Daniel Sun, at the Johns Hopkins Medical Institutions.
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