CataractCompDetect: Intraoperative Complication Detection in Cataract Surgery
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914168786911232 |
|---|---|
| author | Sachdeva, Bhuvan Kumari, Sneha Agarwal, Rudransh Kumaraswamy, Shalaka Prasad, Niharika Singri Mueller, Simon Lechtenboehmer, Raphael Wintergerst, Maximilian W. M. Schultz, Thomas Murali, Kaushik Jain, Mohit |
| author_facet | Sachdeva, Bhuvan Kumari, Sneha Agarwal, Rudransh Kumaraswamy, Shalaka Prasad, Niharika Singri Mueller, Simon Lechtenboehmer, Raphael Wintergerst, Maximilian W. M. Schultz, Thomas Murali, Kaushik Jain, Mohit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18968 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CataractCompDetect: Intraoperative Complication Detection in Cataract Surgery Sachdeva, Bhuvan Kumari, Sneha Agarwal, Rudransh Kumaraswamy, Shalaka Prasad, Niharika Singri Mueller, Simon Lechtenboehmer, Raphael Wintergerst, Maximilian W. M. Schultz, Thomas Murali, Kaushik Jain, Mohit Computer Vision and Pattern Recognition 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. |
| title | CataractCompDetect: Intraoperative Complication Detection in Cataract Surgery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18968 |