A multi-modal approach for efficient, point-of-care screening of hypertrophic cardiomyopathy

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PROJECT SUMMARY Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy, affecting up to 0.5% of the general population. HCM confers an increased risk of morbidity and mortality but remains clinically underrecognized. Traditionally, the diagnosis of HCM has relied on comprehensive assessment by echocardiography or magnetic resonance imaging, modalities which are not available for screening of the general population. As novel disease-modifying therapies emerge, there is a need for efficient strategies to improve HCM screening outside specialized centers. The research proposed in this post-doctoral fellowship will leverage advanced computational methods and the expanding availability of wearable and portable technologies to adapt machine learning algorithms for the efficient, point-of-care screening of HCM. In Aim 1, the applicant proposes to use a large electrocardiographic (ECG) library to adapt ECG signals for use with wearable devices and fine-tune those signals for the detection of HCM. Noising-denoising algorithms and cross-modal pre-training with corresponding echocardiographic and cardiac magnetic resonance videos will ensure that the models are robust to noise and learn key representations of the HCM phenotype, respectively. In Aim 2, single-view, two- dimensional echocardiographic videos will be extracted, pre-processed, and augmented to simulate point-of- care image acquisition. Through a self-supervised, contrastive pre-training approach, the applicant will build data-efficient computational models to screen for HCM based on echocardiographic videos reflecting the quality and unique challenges seen with point-of-care use. In Aim 3, the applicant proposes a prospective case-control study of patients with and without HCM, who will undergo point-of-care electrocardiography and echocardiography, to test the feasibility and real-world performance of a two-stage HCM screening protocol based on Aims 1 and 2. The proposal is supported by strong mentorship from experts in biomedical machine learning, computer vision, and implementation science. The Yale School of Medicine offers the facilities and computational resources necessary to accomplish the research goals, whereas the Yale-New Haven Health electronic health record and well-phenotyped echocardiographic and ECG libraries ensure access to a diverse and representative population. The proposed period of mentored research will support the applicant’s training in computer vision, advanced analytics, and medical informatics. The experience, data, and skillset acquired during this period will further support the applicant in preparing for a successful career in the implementation science of cardiovascular artificial intelligence technologies.