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

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 classification v3.0 for esophageal motility disorders. A gradient class activation map (Grad‑CAM) technique was used. The discrimination model for the control and achalasia groups yielded a 100% correct response rate for evaluating the validation images (n=30). Grad‑CAM analysis revealed that the model focused on the area around the lower esophageal sphincter pressure in type 1 achalasia for differentiation, closely aligning with expert perspectives. An AI‑based HRM imaging assistance system may not only support physicians in distinguishing esophageal motility disorders with improved diagnostic accuracy but also serve as a novel tool that provides deeper clinical insights and highlights key interpretative features in HRM evaluations. Further large‑scale validation is required to confirm its clinical utility.