Using Surgeon Hand Motions to Predict Surgical Maneuvers
| Autor: | Brady L. Miller, Yu Hen Hu, David P. Azari, Robert G. Radwin, Brian Le |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Surgeon hand
Computer science 05 social sciences Video Recording Video record Human Factors and Ergonomics Hand Pattern Recognition Automated Machine Learning 03 medical and health sciences Behavioral Neuroscience 0302 clinical medicine Human–computer interaction Motor Skills 030220 oncology & carcinogenesis Surgical Procedures Operative Image Interpretation Computer-Assisted Surgical skills Humans 0501 psychology and cognitive sciences 050107 human factors Applied Psychology |
| Zdroj: | Human factors. 61(8) |
| ISSN: | 1547-8181 |
| Popis: | Objective: This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations. Background: Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries. Method: We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video. Results: Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants. Conclusion: Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants. Application: Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching. |
| Databáze: | OpenAIRE |
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