# Artificial Intelligence for Determining Genetic Risk of Sudden Cardiac Arrest using the Electrocardiogram

> **NIH NIH F30** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $49,474

## Abstract

PROJECT SUMMARY
Sudden cardiac arrest (SCA) is a leading cause of cardiovascular deaths in the United States, with significant
disparities in survival rates among different racial and ethnic groups. Despite its prevalence, predicting risk of
SCA is challenging due to limited methods for early detection. It has been known that risk of SCA is mediated
by genetic contributions, which can be inherited as monogenic and polygenic factors. Although there exist
techniques for assessing clinical risk, there is currently no viable approach to deduce genetic risk unless through
direct genotyping or genetic sequencing. Furthermore, current clinical genomic testing is constrained in terms of
accessibility, cost, and expert interpretation requirements, especially among underrepresented populations,
leading to a lack of scalable methods for early determination of SCA genetic risk.
The electrocardiogram (ECG) is a non-invasive, widely used tool for measuring cardiac electrical activity. As
biological mechanisms influence waveform patterns that define certain arrhythmias, the ECG has potential to
reveal the genetic underpinnings of the electrical function of the heart. While subtle waveform morphology may
escape human observation, ECG interpretation has been greatly augmented by deep learning (DL) techniques,
allowing for the discernment of subtle waveform patterns of even subclinical disease for which diagnostic
guidelines are lacking. DL has already been leveraged on ECG to identify various cardiac pathologies, such as
hypertrophic cardiomyopathy, low left ventricular ejection fraction, ST elevated myocardial infarction, and
valvular disease, but has yet to be applied to genetic predisposition to SCA. As such, the applications of DL to
ECG waveform promise to provide insight into genotypic foundations of SCA to enable prevention and
prophylactic strategies in cardiac electrophysiology and personalized medicine.
This proposal seeks to develop DL models that bridge the gap between phenotypic ECG patterns and disease
genotypes with a focus on diverse populations. To improve the identification of monogenic inherited syndromes
in individuals of diverse ancestry, a DL model will be leveraged on electronic health record (EHR) and ECG data
to classify those with risk genotypes (Aim 1). To facilitate identification of those at high polygenic risk for SCA,
a separate DL algorithm using EHR and ECG data will be developed for classification of individuals with high
polygenic risk score (PRS) for coronary artery disease and SCA (Aim 2). For Aims 1 and 2, by integrating data
from large repositories of genetic data such as the Mount Sinai Million Health Discoveries Program, All of Us
Research Program, the United Kingdom Biobank, we hypothesize that DL has potential to enable identification
of monogenic and polygenic risk of SCA.
Supported by resources and renowned faculty from the Charles Bronfman Institute for Personalized Medicine at
the Icahn School of Medicine at Mount Si...

## Key facts

- **NIH application ID:** 10998172
- **Project number:** 1F30HL176177-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Joy Jiang
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $49,474
- **Award type:** 1
- **Project period:** 2024-09-30 → 2027-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10998172

## Citation

> US National Institutes of Health, RePORTER application 10998172, Artificial Intelligence for Determining Genetic Risk of Sudden Cardiac Arrest using the Electrocardiogram (1F30HL176177-01). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/10998172. Licensed CC0.

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