Abstract
The diagnosis of cardiovascular illnesses uses multiple modalities in order to obtain a complete and as robust as possible assessment of the heart. However, when addressing distinct pathologies, not all information might be needed in order to achieve a confident-enough diagnosis.
We propose a probabilistic machine learning method to identify the patients for which the acquisition of more complex data would be useful. We hypothesise that there exists a hierarchical relationship between modalities: echocardiography is more accessible and has a lower economical cost than other modalities (like magnetic resonance imaging (MRI)). The framework consists of two classifier models, each predicting the illness from the echocardiographic and MRI views, and a sample-weighting model that combines both predictions. This weighting model is used to decide which individuals will not need an MRI acquisition additional to the echocardiographic examination.
We illustrated this on a dataset of asymptomatic individuals with an echocardiographic study (N = 480), a subset of those also includes a MRI (N = 159). We analyse the effect of being overweight on cardiac geometry. We identified that the type of remodelling depended on blood pressure: overweight combined with high blood pressure resulted in an increase of ventricular mass, while only size changes were preserved for low-pressure individuals. With our method, we established that boundary cases of the former group could be correctly classified after incorporating MRI, while it was not the case for the latter.
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Acknowledgements
This project has been partially funded with support the French ANR (LABEX PRIMES of Univ. Lyon [ANR-11-LABX-0063] and the JCJC project “MIC-MAC” [ANR-19-CE45-0005]), the Erasmus + Programme of the European Union (Framework Agreement number: 2013-0040), “la Caixa” Foundation (LCF/PR/ GN14/10270005, LCF/PR/GN18/10310003), the Instituto de Salud Carlos III (PI14/00226, PI17/00675) integrated in “Plan Nacional de I+D+I” and cofinanciated by ISCIII-Subdirección General de Evaluación and Fondo Europeo de Desarrollo Regional (FEDER) “Una manera de hacer Europa”, and AGAUR 2017 SGR grant #1531.
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Bernardino, G. et al. (2022). Hierarchical Multi-modality Prediction Model to Assess Obesity-Related Remodelling. In: Puyol Antón, E., et al. Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science(), vol 13131. Springer, Cham. https://doi.org/10.1007/978-3-030-93722-5_12
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DOI: https://doi.org/10.1007/978-3-030-93722-5_12
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