International journal of computer assisted radiology and surgery · 2025
Evaluating the generalizability of video-based assessment of intraoperative surgical skill in capsulorhexis.
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Gong Zhiwei, Wan Bohua, Paranjape Jay N, Sikder Shameema, Patel Vishal M, Vedula S Swaroop
Аннотация
PURPOSE: Assessment of intraoperative surgical skill is necessary to train surgeons and certify them for practice. The generalizability of deep learning models for video-based assessment (VBA) of surgical skill has not yet been evaluated. In this work, we evaluated one unsupervised domain adaptation (UDA) and three semi-supervised (SSDA) methods for generalizability of models for VBA of surgical skill in capsulorhexis by training on one dataset and testing on another.
METHODS: We used two datasets, D99 and Cataract-101 (publicly available), and two state-of-the-art models for capsulorhexis. The models include a convolutional neural network (CNN) to extract features from video images, followed by a long short-term memory (LSTM) network or a transformer. We augmented the CNN and the LSTM with attention modules. We estimated accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).
RESULTS: Maximum mean discrepancy (MMD) did not improve generalizability of CNN-LSTM but slightly improved CNN transformer. Among the SSDA methods, Group Distributionally Robust Supervised Learning improved generalizability in most cases.
CONCLUSION: Model performance improved with the domain adaptation methods we evaluated, but it fell short of within-dataset performance. Our results provide benchmarks on a public dataset for others to compare their methods.
METHODS: We used two datasets, D99 and Cataract-101 (publicly available), and two state-of-the-art models for capsulorhexis. The models include a convolutional neural network (CNN) to extract features from video images, followed by a long short-term memory (LSTM) network or a transformer. We augmented the CNN and the LSTM with attention modules. We estimated accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).
RESULTS: Maximum mean discrepancy (MMD) did not improve generalizability of CNN-LSTM but slightly improved CNN transformer. Among the SSDA methods, Group Distributionally Robust Supervised Learning improved generalizability in most cases.
CONCLUSION: Model performance improved with the domain adaptation methods we evaluated, but it fell short of within-dataset performance. Our results provide benchmarks on a public dataset for others to compare their methods.