1. Automated detection of steps in videos of strabismus surgery using deep learning
- Author
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Ce Zheng, Wen Li, Siying Wang, Haiyun Ye, Kai Xu, Wangyi Fang, Yanli Dong, Zilei Wang, and Tong Qiao
- Subjects
Deep learning ,Strabismus surgery ,Automated detection ,Surgical videos ,Ophthalmology ,RE1-994 - Abstract
Abstract Background Learning to perform strabismus surgery is an essential aspect of ophthalmologists’ surgical training. Automated classification strategy for surgical steps can improve the effectiveness of training curricula and the efficient evaluation of residents’ performance. To this end, we aimed to develop and validate a deep learning (DL) model for automated detecting strabismus surgery steps in the videos. Methods In this study, we gathered 479 strabismus surgery videos from Shanghai Children’s Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, spanning July 2017 to October 2021. The videos were manually cut into 3345 clips of the eight strabismus surgical steps based on the International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubrics (ICO-OSCAR: strabismus). The videos dataset was randomly split by eye-level into a training (60%), validation (20%) and testing dataset (20%). We evaluated two hybrid DL algorithms: a Recurrent Neural Network (RNN) based and a Transformer-based model. The evaluation metrics included: accuracy, area under the receiver operating characteristic curve, precision, recall and F1-score. Results DL models identified the steps in video clips of strabismus surgery achieved macro-average AUC of 1.00 (95% CI 1.00–1.00) with Transformer-based model and 0.98 (95% CI 0.97-1.00) with RNN-based model, respectively. The Transformer-based model yielded a higher accuracy compared with RNN-based models (0.96 vs. 0.83, p
- Published
- 2024
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