Artificial intelligence-based automated surgical workflow recognition in esophageal endoscopic submucosal dissection: an international multicenter study (with video).

Background: Endoscopic submucosal dissection (ESD) is a crucial yet challenging multi-phase procedure for treating early gastrointestinal cancers. This study developed an artificial intelligence (AI)-based automated surgical workflow recognition model for esophageal ESD and proposed an innovative training program based on esophageal ESD videos with or without AI labels to evaluate its effectiveness for trainees.

Methods: We retrospectively analyzed complete ESD videos collected from seven hospitals worldwide between 2016 and 2024.

EVALUATION OF ARTIFICIAL INTELLIGENCE-BASED QUANTITATIVE ANALYSIS TO IDENTIFY CLINICALLY SIGNIFICANT SEVERE RETINOPATHY OF PREMATURITY.

Purpose: To evaluate the screening potential of a deep learning algorithm-derived severity score by determining its ability to detect clinically significant severe retinopathy of prematurity (ROP).

Methods: Fundus photographs were collected, and standard panel diagnosis was generated for each examination by combining three independent image-based gradings. All images were analyzed using a deep learning algorithm, and a quantitative assessment of retinal vascular abnormality (DeepROP score) was assigned on a 1 to 100 scale.