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

> **NIH NIH F32** · YALE UNIVERSITY · 2024 · $70,810

## Abstract

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.

## Key facts

- **NIH application ID:** 11031296
- **Project number:** 5F32HL170592-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Evangelos Oikonomou
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $70,810
- **Award type:** 5
- **Project period:** 2023-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11031296

## Citation

> US National Institutes of Health, RePORTER application 11031296, A multi-modal approach for efficient, point-of-care screening of hypertrophic cardiomyopathy (5F32HL170592-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11031296. Licensed CC0.

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