The paper introduces an explainable AI framework for automated skill assessment in cataract surgery, leveraging the world’s largest dataset of 2,000 surgical videos. Using advanced computer vision and signal‑processing techniques, the system extracts ten objective motion‑based metrics that correlate strongly with expert subjective ratings, achieving up to 87% accuracy. The framework’s explainability distinguishes it from prior opaque models, offering transparent, quantitative performance indicators that could complement or replace traditional subjective scoring.
Background: Laparoscopic camera navigation (LCN) is a critical skill, yet its current assessment typically relies on manual rating systems which are time-consuming and difficult to scale. Automated feedback could significantly enhance surgical training by providing immediate, standardized metrics.
arXiv:2608.30872v1 Announce Type: new
Abstract: Objective assessment of surgical technical skill is important for surgical training and structured feedback, but current workflows remain dependent on...
By Chaohui Dang, Zheheng Jiang, James Glasbey, David Luke, Theodoros Arvanitis, Le Zhang
arXiv:2606. 30657v1 Announce Type: cross Abstract: Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself.
By Pietro Mascagni, Lalith Sharan, Deepak Alapatt, Nicolas Padoy
arXiv:2603. 27341v4 Announce Type: replace Abstract: Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but surgical benchmarks in particular are often missing from prominent medical benchmark suites.
By Kirill Skobelev, Eric Fithian, Yegor Baranovski, Jack Cook, Sandeep Angara, Shauna Otto, Zhuang-Fang Yi, John Zhu, Neeraj Mainkar, Margaux Masson-Forsythe, Daniel A. Donoho, X. Y. Han
This study investigates whether vision‑based models for surgical skill assessment learn representations that transfer across different scoring rubrics (GOALS and OSATS) using the LASANA and JIGSAWS datasets. By evaluating end‑to‑end training, Adaptive Sharpness‑Aware Minimization, and self‑supervised/contrastive pretraining, the authors find that models pretrained on JIGSAWS can transfer reasonably well to LASANA, but transfer to JIGSAWS fails, likely due to annotation inconsistencies. Control experiments with a Kinetics‑pretrained backbone show that task‑specific heads carry most of the skill prediction load, while the backbone provides general spatiotemporal features.
By Hanna Hoffmann, Felix von Bechtolsheim, Stefanie Speidel, Rebecca Hisey