Omkar2118/datasets huggingface dataset:
SICS-155
Full name: Phase Recognition in Small Incision Cataract Surgery Videos

Cataract is the leading cause of blindness worldwide, most affecting life in low- and middle-income countries (LMICs). The mainly used, most appropriate, and most cost-effective cataract surgical technique for LMICs is small incision cataract surgery (SICS). While algorithms have been developed for automated video analysis of surgical performance parameters for the cataract surgical technique predominantly used in high-income settings, so far there were no datasets nor algorithms for SICS available. This MICCAI challenge introduces the first SICS video dataset and offers teams the opportunity to evaluate the effectiveness of their phase recognition algorithms. The dataset of 155 patients was recruited at Sankara Eye Hospital in India.

Analysis of surgical phases is important because it allows for quantitative comparison between different surgeons, feedback on identified critical steps, and detection of discrepancies from surgical protocols and because it is the first step for automatic assessment of surgical quality (Sim-OSSCAR). Our contribution is the first public dataset for SICS holding surgical videos and phase annotations of 155 surgeries with 18 distinct phases.

Currently, there are other public cataract surgery phase datasets like Cataract-101 (n=101 videos) or the IEEE Cataracts (n=50 videos) but they only show phacoemulsification surgery which is distinct from SICS.  Despite SICS widespread adoption in countries of the global south, no publicly available dataset exists for for this surgery, leaving a critical gap in cataract surgery research. Competitors are expected to submit an algorithm for predicting surgical phases based on the video data we supply and a short paper describing their approach.