# Opportunistic Atherosclerotic Cardiovascular Disease Risk Estimation at Abdominal CTs with Robust and Unbiased Deep Learning

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $589,072

## Abstract

PROJECT SUMMARY
Atherosclerotic cardiovascular disease (ASCVD) is the main cause of morbidity and mortality worldwide, and
affects 18+ million adults nationally. However, 80% of ASCVD deaths may be prevented with prompt intervention
following early screening for ASCVD risk – a powerful rationale for the unmet need of accurate subclinical
ASCVD diagnoses. Thus, in this study we assess whether a deep learning (DL)-based analysis of pre-existing
abdominal computed tomography (CT) scans paired with electronic medical records (EMR) improves prediction
of cardiovascular death, myocardial infarction, and stroke in a large multi-site primary prevention population. We
will conduct this study in a large, diverse, real-world population with an external validation to ascertain whether
we can improve upon the clinically-utilized pooled cohort equations (PCE) that have numerous shortcomings.
20+ million abdominal CT scans performed annually in the US. While these scans answer specific clinical
questions, quantitative information related to tissue phenotypes associated with cardiometabolic risk is simply
not evaluated. DL algorithms can be used to quantify body composition metrics for adipose tissue, muscle, bone,
liver, and vascular calcifications, which can all be used to improve upon the PCE for determining cardiovascular
events. In aim 1 of our proposal, we will build automated segmentation algorithms with a built-in quality control
mechanism to extract these body composition metrics in 125,000+ diverse subjects to ascertain population-level
normative values of tissue size and radiodensity. In aim 2, we will augment the PCE covariates with these body
composition values and additional EMR features for predicting ASCVD risk with advanced DL models. Moreover,
we will devise new algorithmic approaches for improving health equity by ensuring similar model performance
across patient sub-groups of PCE eligibility, race/ethnicity, insurance type, and CT scanner make/model. In aim
3, we will build a new ASCVD risk estimator that directly uses 3D imaging data. We will augment this end-to-end
prediction approach by integrating multi-modal models that leverage both imaging data and EMR data. Realizing
the need for improved explainability of DL solutions, we will build digital twins of each subject to describe why
model predictions are being made and what changes a patient could make to lower ASCVD risk.
We will train all models on data from Stanford (25k patients), test on data from Stanford (8k patients), and
externally validate the models on data from three Mayo Clinic sites (20k+ patients) to assess the generalizability
of our tools. We have assembled an inter-disciplinary MPI team of DL experts, cardiologists, and abdominal
radiologists to build such ASCVD risk models. We develop innovative tools to improve accuracy, generalizability,
bias, and explainability of DL-based ASCVD risk models. Our long-term goal is to enable early detection of silent
atherosclerosis an...

## Key facts

- **NIH application ID:** 10916202
- **Project number:** 5R01HL167974-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Imon Banerjee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $589,072
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10916202

## Citation

> US National Institutes of Health, RePORTER application 10916202, Opportunistic Atherosclerotic Cardiovascular Disease Risk Estimation at Abdominal CTs with Robust and Unbiased Deep Learning (5R01HL167974-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10916202. Licensed CC0.

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