Tae Soo Kim1, Molly O'Brien1, Sidra Zafar2, Gregory D Hager1, Shameema Sikder2, S Swaroop Vedula3. 1. Johns Hopkins University, 3400 N. Charles Street, Malone Hall 340, Baltimore, MD, 21218, USA. 2. Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe Street, Baltimore, MD, 21287, USA. 3. Johns Hopkins University, 3400 N. Charles Street, Malone Hall 340, Baltimore, MD, 21218, USA. vedula@jhu.edu.
Abstract
PURPOSE: Objective assessment of intraoperative technical skill is necessary for technology to improve patient care through surgical training. Our objective in this study was to develop and validate deep learning techniques for technical skill assessment using videos of the surgical field. METHODS: We used a data set of 99 videos of capsulorhexis, a critical step in cataract surgery. One expert surgeon annotated each video for technical skill using a standard structured rating scale, the International Council of Ophthalmology's Ophthalmology Surgical Competency Assessment Rubric:phacoemulsification (ICO-OSCAR:phaco). Using two capsulorhexis indices in this scale (commencement of flap and follow-through, formation and completion), we specified an expert performance when at least one of the indices was 5 and the other index was at least 4, and novice otherwise. In addition, we used scores for capsulorhexis commencement and capsulorhexis formation as separate ground truths (Likert scale of 2 to 5; analyzed as 2/3, 4 and 5). We crowdsourced annotations of instrument tips. We separately modeled instrument trajectories and optical flow using temporal convolutional neural networks to predict a skill class (expert/novice) and score on each item for capsulorhexis in ICO-OSCAR:phaco. We evaluated the algorithms in a five-fold cross-validation and computed accuracy and area under the receiver operating characteristics curve (AUC). RESULTS: The accuracy and AUC were 0.848 and 0.863 for instrument tip velocities, and 0.634 and 0.803 for optical flow fields, respectively. CONCLUSIONS: Deep neural networks effectively model surgical technical skill in capsulorhexis given structured representation of intraoperative data such as optical flow fields extracted from video or crowdsourced tool localization information.
PURPOSE: Objective assessment of intraoperative technical skill is necessary for technology to improve patient care through surgical training. Our objective in this study was to develop and validate deep learning techniques for technical skill assessment using videos of the surgical field. METHODS: We used a data set of 99 videos of capsulorhexis, a critical step in cataract surgery. One expert surgeon annotated each video for technical skill using a standard structured rating scale, the International Council of Ophthalmology's Ophthalmology Surgical Competency Assessment Rubric:phacoemulsification (ICO-OSCAR:phaco). Using two capsulorhexis indices in this scale (commencement of flap and follow-through, formation and completion), we specified an expert performance when at least one of the indices was 5 and the other index was at least 4, and novice otherwise. In addition, we used scores for capsulorhexis commencement and capsulorhexis formation as separate ground truths (Likert scale of 2 to 5; analyzed as 2/3, 4 and 5). We crowdsourced annotations of instrument tips. We separately modeled instrument trajectories and optical flow using temporal convolutional neural networks to predict a skill class (expert/novice) and score on each item for capsulorhexis in ICO-OSCAR:phaco. We evaluated the algorithms in a five-fold cross-validation and computed accuracy and area under the receiver operating characteristics curve (AUC). RESULTS: The accuracy and AUC were 0.848 and 0.863 for instrument tip velocities, and 0.634 and 0.803 for optical flow fields, respectively. CONCLUSIONS: Deep neural networks effectively model surgical technical skill in capsulorhexis given structured representation of intraoperative data such as optical flow fields extracted from video or crowdsourced tool localization information.
Authors: Tala Al-Khaled; Luis Acaba-Berrocal; Emily Cole; Daniel S W Ting; Michael F Chiang; R V Paul Chan Journal: Asia Pac J Ophthalmol (Phila) Date: 2022-05-01
Authors: Grace L Paley; Rebecca Grove; Tejas C Sekhar; Jack Pruett; Michael V Stock; Tony N Pira; Steven M Shields; Evan L Waxman; Bradley S Wilson; Mae O Gordon; Susan M Culican Journal: J Surg Educ Date: 2021-02-25 Impact factor: 2.891
Authors: Guillermo Sánchez-Brizuela; Francisco-Javier Santos-Criado; Daniel Sanz-Gobernado; Eusebio de la Fuente-López; Juan-Carlos Fraile; Javier Pérez-Turiel; Ana Cisnal Journal: Sensors (Basel) Date: 2022-07-11 Impact factor: 3.847