WetCat: Automating Skill Assessment in Wetlab Cataract Surgery Videos

2025-06-10

AI Summary

Cataract wet-lab surgical training has long relied on subjective, inefficient manual assessment and lacks standardized, benchmark evaluation datasets compatible with controllable training environments. Method: We introduce the first automated surgical skill assessment dataset for cataract wet-lab training—Synapse ID: syn66401174—comprising high-definition artificial-eye surgical videos, fine-grained procedural phase annotations (focusing on capsulorhexis and phacoemulsification), and anatomical structure semantic segmentation. Our labeling framework is clinically aligned, enabling surgical phase recognition, temporal modeling, and interpretable AI-based evaluation. Contribution/Results: This dataset fills a critical gap in objective, scalable skill assessment within wet-lab settings and establishes a foundational resource for developing intelligent surgical education tools. It supports reproducible benchmarking of AI-driven assessment models and advances data-driven competency evaluation in ophthalmic surgical training.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance

Abstract

To meet the growing demand for systematic surgical training, wetlab environments have become indispensable platforms for hands-on practice in ophthalmology. Yet, traditional wetlab training depends heavily on manual performance evaluations, which are labor-intensive, time-consuming, and often subject to variability. Recent advances in computer vision offer promising avenues for automated skill assessment, enhancing both the efficiency and objectivity of surgical education. Despite notable progress in ophthalmic surgical datasets, existing resources predominantly focus on real surgeries or isolated tasks, falling short of supporting comprehensive skill evaluation in controlled wetlab settings. To address these limitations, we introduce WetCat, the first dataset of wetlab cataract surgery videos specifically curated for automated skill assessment. WetCat comprises high-resolution recordings of surgeries performed by trainees on artificial eyes, featuring comprehensive phase annotations and semantic segmentations of key anatomical structures. These annotations are meticulously designed to facilitate skill assessment during the critical capsulorhexis and phacoemulsification phases, adhering to standardized surgical skill assessment frameworks. By focusing on these essential phases, WetCat enables the development of interpretable, AI-driven evaluation tools aligned with established clinical metrics. This dataset lays a strong foundation for advancing objective, scalable surgical education and sets a new benchmark for automated workflow analysis and skill assessment in ophthalmology training. The dataset and annotations are publicly available in Synapse https://www.synapse.org/Synapse:syn66401174/files.

Problem

• Automating skill assessment in wetlab cataract surgery videos
• Reducing manual evaluation labor and variability in surgical training
• Lacking datasets for comprehensive wetlab skill evaluation

Innovation

• Automated skill assessment using computer vision
• First wetlab cataract surgery video dataset
• AI-driven tools for standardized surgical evaluation