CataractCompDetect: Intraoperative Complication Detection in Cataract Surgery

TL;DR

This paper introduces CataractCompDetect, an automated framework for detecting intraoperative complications in cataract surgery videos, utilizing advanced localization, tracking, risk scoring, and reasoning techniques, validated on a new annotated dataset.

Contribution

The work presents the first annotated cataract surgery video dataset and a novel detection framework combining multiple AI techniques for intraoperative complication recognition.

Findings

Achieved an average F1 score of 70.63% on the dataset.

High accuracy for iris prolapse detection at 81.8%.

Demonstrated the effectiveness of vision-language reasoning in surgical event detection.

Abstract

Cataract surgery is one of the most commonly performed surgeries worldwide, yet intraoperative complications such as iris prolapse, posterior capsule rupture (PCR), and vitreous loss remain major causes of adverse outcomes. Automated detection of such events could enable early warning systems and objective training feedback. In this work, we propose CataractCompDetect, a complication detection framework that combines phase-aware localization, SAM 2-based tracking, complication-specific risk scoring, and vision-language reasoning for final classification. To validate CataractCompDetect, we curate CataComp, the first cataract surgery video dataset annotated for intraoperative complications, comprising 53 surgeries, including 23 with clinical complications. On CataComp, CataractCompDetect achieves an average F1 score of 70.63%, with per-complication performance of 81.8% (Iris Prolapse), 60.87% (PCR), and 69.23% (Vitreous Loss). These results highlight the value of combining structured surgical priors with vision-language reasoning for recognizing rare but high-impact intraoperative events. Our dataset and code will be publicly released upon acceptance.