Real-Time Artificial Intelligence Assistance for Safe Laparoscopic Cholecystectomy: Early-Stage Clinical Evaluation

Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety

Minimally invasive image-guided surgery heavily relies on vision. Deep learning models for surgical video analysis could therefore support visual tasks such as assessing the critical view of safety (CVS) in laparoscopic cholecystectomy (LC), potentially contributing to surgical safety and efficiency. However, the performance, reliability and reproducibility of such models are deeply dependent on the quality of data and annotations used in their development. Here, we present a protocol, checklists, and visual examples to promote consistent annotation of hepatocystic anatomy and CVS criteria. We…

高 stakes 领域中基于元学习的单样本技能评估

One-shot skill assessment in high-stakes domains with limited data via meta learning

提出 A-VBANet 元学习模型，通过单样本学习实现领域无关的手术技能评估，在模拟和真实手术中分别达到 99.5% 和 89.7% 的准确率。

基于压缩不确定性的实时腹腔镜视频检索

Live Laparoscopic Video Retrieval with Compressed Uncertainty

本文提出一种轻量级方法，通过压缩不确定性来缓解视频哈希中不可靠位的影响，实现实时腹腔镜视频检索，并在多任务手术基准上取得3-4%的前10平均精度提升，验证了其泛化能力。

TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems. In this paper, we propose, for the first time in workflow analysis, a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Causal, dilated convolutions allow for a large receptive field and online inference with smooth predictions even during ambiguous transitions. Our method is thoroughly evaluated on two datasets of laparoscopi…