# Deep learning enhanced detection and personalized monitoring of aortic stenosis - The DETECT-AS Study

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $813,824

## Abstract

PROJECT SUMMARY
This is a new application by an early-stage investigator with a long-term career objective of transforming
cardiovascular care using artificial intelligence and data science. The proposal focuses on aortic stenosis (AS),
a progressive narrowing of the aortic valve, which manifests in older adults and causes significant disability and
premature mortality despite minimally invasive treatment strategies. AS is either diagnosed following symptom-
driven diagnostic testing or incidentally discovered, which has simultaneously led to a vast underdiagnosis of
advanced stages of AS while identifying many with early-stage aortic valve disease without clarity on appropriate
follow-up. There is a critical need for novel screening and prognostication strategies for AS. We show that artificial
intelligence (AI) models applied to 1-lead electrocardiograms (AI-ECGs) can be a sensitive and convenient
screen for advanced (moderate/severe) AS. AI-ECG can be paired with a second, more specific, AI-enhanced
handheld cardiac point-of-care ultrasound (POCUS). This AI-POCUS automates the diagnosis of advanced AS
without specialized imaging or expert evaluation. In Aim 1, we propose a multicenter pragmatic RCT evaluating
this 2-stage, AI-driven screening strategy for advanced AS. This innovative, technology-driven screening strategy
will define a new paradigm for the efficient identification of advanced AS. In addition, we evaluate a novel strategy
to bridge the critical gap in precision follow-up, especially for early-stage aortic valve disease. Early aortic valve
disease – aortic sclerosis or mild AS – affects nearly a fourth of older adults over 65 years. However, there are
no guideline recommendations on follow-up for aortic sclerosis, and recommendations for mild AS do not account
for the substantial heterogeneity in disease progression. In our preliminary investigations from a multicenter
observational cohort study, we show that a deep learning tool for echocardiographic videos – deep learning-
based aortic stenosis severity index (DASSi) – can define those at substantially elevated risk of progression to
advanced AS and adverse clinical outcomes. In Aim 2, we will conduct a multicenter, prospective evaluation of
an individualized AS progression score among older adults with aortic sclerosis or mild AS through a protocolized
Doppler echocardiogram to distinguish those with high and low rates of progression. The investigations in Aim 2
will establish the reliability of a digital biomarker for AS progression that can enable precision care and follow-
up. The work is supported by the team’s broad expertise in (a) clinical medicine, including cardiology, geriatrics,
and imaging; (b) technology, spanning informatics, data science, and AI; and (c) clinical trials, with experience in
designing and executing studies. The evidence generated from a multicenter evaluation of low-cost AI-driven
interventions can be immediately adopted and scaled to have a major...

## Key facts

- **NIH application ID:** 10944019
- **Project number:** 1R01AG089981-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Rohan Khera
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $813,824
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10944019

## Citation

> US National Institutes of Health, RePORTER application 10944019, Deep learning enhanced detection and personalized monitoring of aortic stenosis - The DETECT-AS Study (1R01AG089981-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10944019. Licensed CC0.

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