Renji endoscopic submucosal dissection video data set for Esophagus

Progress in artificial intelligence has markedly improved computer-supported interventions, professional education for surgeons, and post-procedural analysis of operative recordings, considerably enhancing surgical proficiency and clinical outcomes. Phase recognition frameworks for endoscopic procedures utilizing deep learning methodologies depend critically on well-annotated comprehensive datasets. Our study introduces the Renji database containing endoscopic submucosal dissection (ESD) videos for esophageal lesions, comprising 25 procedural recordings with 141,909 phase-specific classifications jointly annotated by a specialized endoscopy team. To the best of our knowledge, this constitutes the inaugural publicly available collection of esophageal ESD videos with comprehensive phase annotations, and we believe this contribution will assist in establishing benchmarks for future esophageal ESD databases. The complete video repository and associated annotations have been made freely accessible to researchers through the Figshare repository.