OBJECTIVE: Computer vision was used to predict expert performance ratings from surgeon hand motions for tying and suturing tasks. SUMMARY BACKGROUND DATA: Existing methods, including the objective structured assessment of technical skills (OSATS), have proven reliable, but do not readily discriminate at the task level. Computer vision may be used for evaluating distinct task performance throughout an operation. METHODS: Open surgeries was videoed and surgeon hands were tracked without using sensors or markers. An expert panel of 3 attending surgeons rated tying and suturing video clips on continuous scales from 0 to 10 along 3 task measures adapted from the broader OSATS: motion economy, fluidity of motion, and tissue handling. Empirical models were developed to predict the expert consensus ratings based on the hand kinematic data records. RESULTS: The predicted versus panel ratings for suturing had slopes from 0.73 to 1, and intercepts from 0.36 to 1.54 (Average R2 = 0.81). Predicted versus panel ratings for tying had slopes from 0.39 to 0.88, and intercepts from 0.79 to 4.36 (Average R2 = 0.57). The mean square error among predicted and expert ratings was consistently less than the mean squared difference among individual expert ratings and the eventual consensus ratings. CONCLUSIONS: The computer algorithm consistently predicted the panel ratings of individual tasks, and were more objective and reliable than individual assessment by surgical experts.
OBJECTIVE: Computer vision was used to predict expert performance ratings from surgeon hand motions for tying and suturing tasks. SUMMARY BACKGROUND DATA: Existing methods, including the objective structured assessment of technical skills (OSATS), have proven reliable, but do not readily discriminate at the task level. Computer vision may be used for evaluating distinct task performance throughout an operation. METHODS: Open surgeries was videoed and surgeon hands were tracked without using sensors or markers. An expert panel of 3 attending surgeons rated tying and suturing video clips on continuous scales from 0 to 10 along 3 task measures adapted from the broader OSATS: motion economy, fluidity of motion, and tissue handling. Empirical models were developed to predict the expert consensus ratings based on the hand kinematic data records. RESULTS: The predicted versus panel ratings for suturing had slopes from 0.73 to 1, and intercepts from 0.36 to 1.54 (Average R2 = 0.81). Predicted versus panel ratings for tying had slopes from 0.39 to 0.88, and intercepts from 0.79 to 4.36 (Average R2 = 0.57). The mean square error among predicted and expert ratings was consistently less than the mean squared difference among individual expert ratings and the eventual consensus ratings. CONCLUSIONS: The computer algorithm consistently predicted the panel ratings of individual tasks, and were more objective and reliable than individual assessment by surgical experts.
Authors: Anne-Lise D D'Angelo; Drew N Rutherford; Rebecca D Ray; Shlomi Laufer; Calvin Kwan; Elaine R Cohen; Andrea Mason; Carla M Pugh Journal: Am J Surg Date: 2015-01-14 Impact factor: 2.565
Authors: Chia-Hsiung Chen; David P Azari; Yu Hen Hu; Mary J Lindstrom; Darryl Thelen; Thomas Y Yen; Robert G Radwin Journal: Ergonomics Date: 2015-06-18 Impact factor: 2.778
Authors: David P Azari; Carla M Pugh; Shlomi Laufer; Calvin Kwan; Chia-Hsiung Chen; Thomas Y Yen; Yu Hen Hu; Robert G Radwin Journal: Hum Factors Date: 2015-11-06 Impact factor: 2.888
Authors: Andre Esteva; Katherine Chou; Serena Yeung; Nikhil Naik; Ali Madani; Ali Mottaghi; Yun Liu; Eric Topol; Jeff Dean; Richard Socher Journal: NPJ Digit Med Date: 2021-01-08
Authors: Kyle Lam; Junhong Chen; Zeyu Wang; Fahad M Iqbal; Ara Darzi; Benny Lo; Sanjay Purkayastha; James M Kinross Journal: NPJ Digit Med Date: 2022-03-03
Authors: Yifan Li; Mitchell D Wolf; Amol D Kulkarni; James Bell; Jonathan S Chang; Amit Nimunkar; Robert G Radwin Journal: Hum Factors Date: 2020-04-14 Impact factor: 2.888