Deep Learning-Based Spatiotemporal Analysis of Cataract Surgery Videos for Surgical Risk Assessment

Postoperative complications remain a significant challenge in vitrectomy surgery, often leading to impaired visual outcomes and repeated interventions. This study presents a deep learning framework for predicting surgical risk by extracting spatiotemporal features from intraoperative microscopy videos. During preprocessing, video enhancement techniques, including motion stabilization, illumination normalization, and cGAN-based artifact suppression, are applied to improve input quality. Contour-adaptive segmentation is employed to delineate relevant surgical regions, followed by Graph Convolutional Networks for structure-aware feature extraction. An Adaptive Sunflower Optimization approach is integrated to refine feature selection, and a transformer-based model captures temporal dependencies for final risk prediction. The framework is evaluated on two public datasets, CaDIS and SICS-105. The model achieved 98.9% accuracy, 97.5% precision, 98.2% recall, 97.9% F1-score, and 98.1% AUC on the CaDIS dataset. Furthermore, the segmentation module attained 98.9% pixel accuracy, 98.5% per-class accuracy, and 96.6% mIoU. The model achieved 99.1% accuracy, 98.5% F1-score, 98.7% sensitivity, 98.7% specificity, and an ROC AUC of 99.10% on the SICS-105 dataset. These results demonstrate consistent improvements over baseline models. Overall, integrating GAN-enhanced preprocessing, graph-based feature learning, optimization, and transformer modeling enhances predictive reliability. This approach highlights the potential of intraoperative video analysis for real-time clinical decision support and improved postoperative risk stratification.