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Closure of mucosal defects following colorectal endoscopic submucosal dissection (C‐ESD) is often performed to prevent post‐C‐ESD adverse events. However, large mucosal defect closure using conventional clips remains technically challenging. Here, we evaluated the feasibility of the novel endoclip with anchor prongs, called the MANTIS Clip (Boston Scientific, Tokyo, Japan), for mucosal defect closure after C‐ESD. This high‐volume retrospective study was conducted at a single center. From March until December 2023, consecutive patients who underwent post‐C‐ESD mucosal defect closure using MANTIS Clip to achieve complete closure were enrolled. Patient clinical characteristics and outcomes were evaluated. Closure of the mucosal defect using the MANTIS Clip was attempted following C‐ESD in 32 lesions. The median sizes of the resection specimens and the tumors were 32 mm (range, 17–100 mm) and 23.5 mm (range, 5–96 mm), respectively. The lesions were distributed between the cecum, ascending, transverse, descending, sigmoid, and rectum. Complete closure was achieved in 96.9% of cases (31/32). All lesions up to 61 mm in defect size were completely closed. The median closure time was 7.9 (range, 3.3–18.0) min. The median numbers of MANTIS Clip and additional conventional clips were 3 (range, 1–4) and 5 (range, 1–11), respectively. No adverse events associated with closure, post‐ESD bleeding, and delayed perforation occurred. MANTIS Clip closure for large post‐C‐ESD mucosal defects was found to be feasible and reliable with a high complete closure rate and a short procedure time. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deep‐leaning (DL) technologies have been applied more dominantly than other traditional machine‐learning methods. They have demonstrated excellent capability to retract visual features of objectives, even unnoticeable ones for humans, and analyze huge amounts of information within short periods. The amount of research applying DL‐based models to real‐time computer‐aided diagnosis (CAD) systems has been increasing steadily in the GI endoscopy field. An array of published data has already demonstrated the advantages of DL‐based CAD models in the detection and characterization of various neoplastic lesions, regardless of the level of the GI tract. Although the diagnostic performances and study designs vary widely, owing to a lack of academic standards to assess the capability of AI for GI endoscopic diagnosis fairly, the superiority of CAD models has been demonstrated for almost all applications studied so far. Most of the challenges associated with AI in the endoscopy field are general problems for AI models used in the real world outside of medical fields. Solutions have been explored seriously and some solutions have been tested in the endoscopy field. Given that AI has become the basic technology to make machines react to the environment, AI would be a major technological paradigm shift, for not only diagnosis but also treatment. In the near future, autonomous endoscopic diagnosis might no longer be just a dream, as we are witnessing with the advent of autonomously driven electric vehicles. [ABSTRACT FROM AUTHOR]