Health AI Central
Peer-Reviewed Publication
Int J Comput Assist Radiol Surg2026August 10, 2026Journal Article

Multimodal graph-based classification of esophageal motility disorders.

Alexander Geiger1, Lars Wagner2, Daniel Rueckert3,4, Alois Knoll5, Dirk Wilhelm6,7, Alissa Jell6,7
1Technical University of Munich, TUM School of Medicine and Health, Research Group MITI, TUM University Hospital, Munich, Germany. alexander.geiger@tum.de.
2Technical University of Munich, TUM School of Medicine and Health, Research Group MITI, TUM University Hospital, Munich, Germany. lars.wagner@tum.de.
3Technical University of Munich, TUM School of Computation, Information and Technology, Chair for AI in Healthcare and Medicine, TUM University Hospital, Munich, Germany.
4Imperial College London, Department of Computing, London, UK.
5Technical University of Munich, TUM School of Computation, Information and Technology, Chair of Robotics, Artificial Intelligence and Real-Time Systems, Munich, Germany.
6Technical University of Munich, TUM School of Medicine and Health, Research Group MITI, TUM University Hospital, Munich, Germany.
7Technical University of Munich, TUM School of Medicine and Health, Department of Surgery, TUM University Hospital, Munich, Germany.
PubMed DOI

Abstract

PURPOSE: Diagnosing esophageal motility disorders, including dysphagia, pose significant challenges due to the complexity of high-resolution impedance manometry (HRIM) data and variability in clinical interpretation. Thiswork explores the feasibility of a multimodal machine learning (ML)-based classification approach that combines HRIM recordings with patient-specific information and incorporates…

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