Health AI Central
Peer-Reviewed Publication
Commun Med (Lond)2025;5(1):513.December 2, 2025Journal Article

A deep learning-based approach to enhance accuracy and feasibility of long-term high-resolution manometry examinations.

Alexander Geiger1, Lars Wagner2, Daniel Rueckert3,4, Dirk Wilhelm2,5, Alissa Jell2,5
1Research Group MITI, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany. alexander.geiger@tum.de.
2Research Group MITI, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
3Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany.
4Department of Computing, Imperial College London, London, United Kingdom.
5Department of Surgery, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
PubMed PMC DOI PDF

Abstract

BACKGROUND: High-resolution manometry (HRM) is the gold standard for diagnosing esophageal motility disorders. However, its short-term laboratory setting often fails to capture intermittent abnormalities. Long-term HRM (LTHRM, up to 24h) provides richer insights into swallowing behavior, but the resulting data volume is immense. Manual analysis by medical experts is laborious, time-consuming, and…

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