Development of a diagnostic model for esophageal achalasia assessed by esophageal high-resolution manometry using artificial intelligence.

ABSTRACT: Esophageal high-resolution manometry (HRM) is an important tool for diagnosing and assessing esophageal achalasia. Artificial intelligence (AI)-assisted HRM image processing has the potential to aid in the diagnosis of esophageal achalasia. However, addressing the challenges associated with the 'black-box' problem is important. In the present study, an automated system that utilizes AI with class-activation maps to highlight diagnostic areas in HRM images was developed. A total of 211 HRM images, which led to the diagnosis of controls and patients with achalasia, were used to train the system using Resnet34, a convolutional neural network model. The diagnoses included normal, type I achalasia, type II achalasia, type III achalasia and hypercontractile esophagus based on the Chicago classif

SUBMITTER: Tabuchi M 

PROVIDER: S-EPMC12856780 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature