Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification

Deep learning now enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction 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 (e.g., variables from electronic health records), we propose a method that considers all descriptors extracted from medical records and echocardiograms to learn the representation of a difficult-to-characterize cardiovascular pathology, 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 a pretext task of predicting a clinical rating. This pretext task is formulated as an ordinal classification to enforce a pathological continuum in the representation space. We observe the major trends along this continuum for a cohort of 239 hypertensive patients to describe, with unprecedented gradation, the effect of hypertension on a number of cardiac function descriptors. Our analysis shows that i) pretrained weights from a foundation model allow to reach good performance (83% accuracy) even with limited data (less than 200 training samples), ii) trends across the population are reproducible between trainings, and iii) for descriptors whose interactions with hypertension are well documented, patterns are consistent with prior physiological knowledge.