Title
Objective assessment of intraoperative technical skill in capsulorhexis using videos of cataract surgery.
Abstract
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. 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). 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. 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.
Year
DOI
Venue
2019
10.1007/s11548-019-01956-8
International Journal of Computer Assisted Radiology and Surgery
Keywords
Field
DocType
Surgical skill assessment, Neural networks, Deep learning, Capsulorhexis, Cataract surgery, Tool trajectories, Crowdsourcing
Cataract surgery,Crowdsourcing,Patient care,Medical physics,Artificial intelligence,Radiology,Deep learning,Medicine,Surgical training,Capsulorhexis
Journal
Volume
Issue
ISSN
14
6
1861-6410
Citations 
PageRank 
References 
1
0.37
0
Authors
6
Name
Order
Citations
PageRank
Tae Soo Kim111.05
Molly O'Brien210.37
Sidra Zafar310.37
Hager Gregory D41946159.37
Shameema Sikder510.37
S Swaroop Vedula6727.60