Academic Journal
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
| Τίτλος: | Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification |
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| Συγγραφείς: | Painchaud, Nathan, Stym-Popper, Jérémie, Courand, Pierre-Yves, Thome, Nicolas, Jodoin, Pierre-Marc, Duchateau, Nicolas, Bernard, Olivier |
| Συνεισφορές: | Duchateau, Nicolas |
| Πηγή: | IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 72:1388-1400 |
| Publication Status: | Preprint |
| Στοιχεία εκδότη: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Έτος έκδοσης: | 2025 |
| Θεματικοί όροι: | [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer Vision and Pattern Recognition (cs.CV), Data models, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Adaptation models, Deep learning, 3. Good health, Machine Learning (cs.LG), Machine Learning, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Artificial Intelligence (cs.AI), Artificial Intelligence, Transformers, Medical services, Hypertension, Pathology, Feature extraction, Training, Computer Vision and Pattern Recognition, Data mining |
| Περιγραφή: | Deep learning 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, 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 i) the XTab foundation model's architecture allows to reach outstanding performance (96.8% AUROC) even with limited data (less than 200 training samples), ii) stratification across the population is reproducible between trainings (within 5.7% mean absolute error), and iii) 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. Code is available at https://github.com/creatis-myriad/didactic. 13 pages + 2 pages of supplementary material, accepted for publication in IEEE TUFFC |
| Τύπος εγγράφου: | Article |
| ISSN: | 1525-8955 0885-3010 |
| DOI: | 10.1109/tuffc.2025.3600902 |
| DOI: | 10.48550/arxiv.2401.07796 |
| Σύνδεσμος πρόσβασης: | http://arxiv.org/abs/2401.07796 |
| Rights: | IEEE Copyright CC BY |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....1f100fbd396f07173c20a4d613aaf925 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 15258955 08853010 |
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| DOI: | 10.1109/tuffc.2025.3600902 |