CataractCompDetect: Intraoperative Complication Detection in Cataract Surgery
Bhuvan Sachdeva, Sneha Kumari, Rudransh Agarwal, Shalaka Kumaraswamy, Niharika Singri Prasad, Simon Mueller, Raphael Lechtenboehmer, Maximilian W. M. Wintergerst, Thomas Schultz, Kaushik Murali, Mohit Jain

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),…
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Taxonomy
TopicsIntraocular Surgery and Lenses · Surgical Simulation and Training · Multimodal Machine Learning Applications
