Constructing Predictive Surgical Path for AI-based Capsulorhexis Skill Transfer
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
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.
The paper introduces SurgFUTR, a state‑change learning framework for predicting future events in endoscopic videos. Instead of forecasting raw observations, it classifies transitions between current and future states using a teacher‑student architecture and an Action Dynamics module. The authors also present SFPBench, a benchmark with five short‑ and long‑term prediction tasks, and demonstrate consistent improvements across multiple datasets and procedures, including cross‑procedure transfer.
SurgTransfer introduces a multi-step latent action modeling framework for surgical world models, enabling the learning of context-invariant latent actions from unlabeled videos. Unlike prior work that relies on ground-truth kinematics or expert annotations, SurgTransfer captures the hierarchical, goal-directed structure of surgical procedures. Experiments on SurgToolLoc-2022 and PitVis-2023 datasets demonstrate improved quantitative metrics and qualitative expert evaluation, with code publicly available on GitHub.
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.
arXiv:2608. 11204v1 Announce Type: cross Abstract: Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.
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.