Automated artificial intelligence-based phase-recognition system for esophageal endoscopic submucosal dissection (with video)

Background and Aims: Endoscopic submucosal dissection (ESD) for super fi cial esophageal cancer is a multistep treatment involving several endoscopic processes. Although analyzing each phase separately is worthwhile, it is not realistic in practice owing to the need for considerable manpower. To solve this problem, we aimed to establish a state-of-the-art arti fi cial intelligence (AI) - based system, speci fi cally, an automated phase-recognition system that can automatically identify each endoscopic phase based on video images. Methods: Ninety-four videos of ESD procedures for super fi cial esophageal cancer were evaluated in this singlecenter study. A deep neural network - based phase-recognition system was developed in an automated manner to recognize each of the endoscopic phases. The system was trained with the use of videos that were annotated and veri fi ed by 2 GI endoscopists. Results: The overall accuracy of the AI model for automated phase recognition was 90%, and the average precision, recall, and F value rates were 91%, 90%, and 90%, respectively. Two representative ESD videos predicted by the model indicated the usability of AI in clinical practice. Conclusions: We demonstrated that an AI -based automated phase -recognition system for esophageal ESD can be established with high accuracy. To the best of our knowledge, this is the fi rst report on automated recognition of ESD treatment phases. Because this system enabled a detailed analysis of phases, collecting large volumes of data in the future may help to identify quality indicators for treatment techniques and uncover unmet medical needs that necessitate the creation of new treatment methods and devices.