Intraoperative Adverse Event Detection in Laparoscopic Surgery: Stabilized Multi-Stage Temporal Convolutional Network with Focal-Uncertainty Loss

Haiqi Wei, Frank Rudzicz, David Fleet, Teodor Grantcharov, Babak Taati

Proceedings of Machine Learning Research 149:1-24, 2021


Intraoperative adverse events (iAEs) increase rates of postoperative mortality and morbidity. Identifying iAEs is important to quality assurance and postoperative care, but requires expertise, is time consuming, and expensive. Automated or partially-automated techniques are, therefore, desirable. Previous work showed that conventional image processing has not worked well with real-world laparoscopic videos. We present a novel modular deep learning system that can partially automate the process of iAE screening using videos of laparoscopic procedures. The system consists of a stabilizer to reduce camera motion, a spatiotemporal feature extractor, and a multi-stage temporal convolutional neural network to detect adverse events. We apply a novel focal-uncertainty smoothing loss to handle class imbalance and to address multi-task uncertainty. The system is evaluated using 5-fold cross-validation on a large (228 hours) dataset of laparoscopic videos, and we perform ablation studies to investigate the effects of stabilization and focal-uncertainty loss. Our system achieves an AUROC of 0.952, an average precision (AP) of 0.626 in thermal injury detection, and an AUROC of 0.823 and an AP of 0.336 in bleeding detection. Our novel modular deep learning system outperforms conventional deep learning baselines. The model can be used as a screening tool to search for high risk events and to provide feedback for operation quality improvements and postoperative care. Source code available on GitHub: