Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification

Nathan Painchaud, Jérémie Stym-Popper, Pierre-Yves Courand, Nicolas Thome, Pierre-Marc Jodoin, Nicolas Duchateau, Olivier Bernard

Abstract: Deep learning enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction (EF) or strain. These descriptors provide fine-grained information that physicians consider, in conjunction with more global variables from the clinical record, to assess patients’ condition. Drawing on novel Transformer models applied to tabular data, we propose a method that considers all descriptors extracted from medical records and echocardiograms to learn the representation of a cardiovascular pathology with a difficult-to-characterize continuum, namely hypertension. Our method first projects each variable into its own representation space using modality-specific approaches. These standardized representations of multimodal data are then fed to a Transformer encoder, which learns to merge them into a comprehensive representation of the patient through the task of predicting a clinical rating. This stratification task is formulated as an ordinal classification to enforce a pathological continuum in the representation space. We observe the major trends along this continuum on a cohort of 239 hypertensive patients, providing unprecedented details in the description of hypertension’s impact on various cardiac function descriptors. Our analysis shows that: 1) the XTab foundation model’s architecture allows to reach high performance (96.8% AUROC) even with limited data (less than 200 training samples); 2) stratification across the population is reproducible between trainings [within 5.7% of mean absolute error (MAE)]; and 3) patterns emerge in descriptors, some of which align with established physiological knowledge about hypertension, while others could pave the way for a more comprehensive understanding of this pathology. The code is available at https://github.com/creatis-myriad/didactic

External IDs:doi:10.1109/tuffc.2025.3600902