Multimodal graph-based classification of esophageal motility disorders

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 a graph-based modeling of esophageal physiology.

We analyze HRIM recordings with corresponding patient information from 104 patientswith esophageal motility disorders collected atTUMUniversity Hospital. Patient data include demographic, clinical, and symptom information extracted from structured questionnaires and free-text notes using keyword detection and large language model-based processing. HRIM data are represented as spatiotemporal graphs, where nodes correspond to pressure values along the esophagus and edges encode spatial adjacency and impedance dynamics.Agraph neural network (GNN) is applied to learn physiologically meaningful representations,which are fused with patient embeddings for multi-category, multi-class classification of swallow events. The impact of patient features and graphbased modeling is evaluated by ablation studies and comparison to vision-based classifier baselines.

The proposed multimodal approach, incorporating patient-specific information, indicates improvements over models that rely solely on HRIM-derived features across all classification categories. Additionally, the graph-based modeling provides gains compared to vision-based baselines. Our experiments systematically assess the complementary contribution of multiple modalities, as well as demonstrate the feasibility of our proposed graph-based approach.

Our initial findings demonstrate that integrating patient-level data with graph-based representations of HRIM signals appears to be a promising direction for more accurate classification of esophageal motility disorders. For further validation, future studies should include larger and more representative datasets to confirm these trends and ensure generalizability.