Cataract-LMM: Large-Scale, Multi-Source, Multi-Task Benchmark for Deep Learning in Surgical Video Analysis

• URL: http://arxiv.org/abs/2510.16371v1
• Date: Sat, 18 Oct 2025 06:48:29 GMT
• Title: Cataract-LMM: Large-Scale, Multi-Source, Multi-Task Benchmark for Deep Learning in Surgical Video Analysis
• Authors: Mohammad Javad Ahmadi, Iman Gandomi, Parisa Abdi, Seyed-Farzad Mohammadi, Amirhossein Taslimi, Mehdi Khodaparast, Hassan Hashemi, Mahdi Tavakoli, Hamid D. Taghirad,
• Abstract summary: We present a dataset of 3,000 cataract surgery videos from two surgical centers, performed by surgeons with a range of experience levels.<n>This resource is enriched with four annotation layers: temporal surgical phases, instance segmentation of instruments and anatomical structures, instrument-tissue interaction tracking, and quantitative skill scores.<n>The technical quality of the dataset is supported by a series of benchmarking experiments for key surgical AI tasks.
• Score: 4.318540086708654
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The development of computer-assisted surgery systems depends on large-scale, annotated datasets. Current resources for cataract surgery often lack the diversity and annotation depth needed to train generalizable deep-learning models. To address this gap, we present a dataset of 3,000 phacoemulsification cataract surgery videos from two surgical centers, performed by surgeons with a range of experience levels. This resource is enriched with four annotation layers: temporal surgical phases, instance segmentation of instruments and anatomical structures, instrument-tissue interaction tracking, and quantitative skill scores based on the established competency rubrics like the ICO-OSCAR. The technical quality of the dataset is supported by a series of benchmarking experiments for key surgical AI tasks, including workflow recognition, scene segmentation, and automated skill assessment. Furthermore, we establish a domain adaptation baseline for the phase recognition task by training a model on a subset of surgical centers and evaluating its performance on a held-out center. The dataset and annotations are available in Google Form (https://docs.google.com/forms/d/e/1FAIpQLSfmyMAPSTGrIy2sTnz0-TMw08ZagTimRulbAQcWdaPwDy187A/viewform ?usp=dialog).

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