Multicenter Validation of Video-based Deep Learning to Evaluate Defecation Patterns on 3-dimensional High-definition Anorectal Manometry.

Background & Aims: Deep learning technologies have demonstrated the ability to identify dyssynergic defecation for diagnosis of common gastrointestinal motility disorders through nuanced interpretation of 3-dimensional high-definition anal manometry (3D-HDAM). We aimed to validate a deep learning algorithm capable of spatiotemporal analysis of 3D-HDAM in a multicenter setting.

Methods: We included 1214 consecutive anorectal manometry studies performed across 3 large health care systems between 2018 and 2022.

Gastric peroral endoscopic myotomy for management of refractory gastroparesis in patients with gastric neurostimulator devices: a multicenter retrospective case control study.

Background And Aims: Gastric neurostimulation (GNS) and gastric peroral myotomy (G-POEM), therapies for refractory gastroparesis, are associated with suboptimal outcomes. We studied the role of G-POEM as a salvage therapy in patients with refractory symptoms after GNS implantation.

Methods: This was a multicenter, retrospective, matched case-control study.