# Improving cardiovascular image-based phenotyping using emerging methods in artificial intelligence

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $807,062

## Abstract

Summary / Abstract
Objective — The goal of this proposal is to develop and optimize novel deep learning (DL) assisted approaches
to improve diagnosis and clinical decision-making for congenital heart disease (CHD). This will be achieved by
using DL, machine learning (ML), and related methods to extract diagnosis, biometric characterizations, and
other information from fetal ultrasound imaging. Notably, this work includes a clinical translational evaluation of
these methods in a population-wide imaging collection spanning two decades, tens of thousands of patients, and
several clinical centers. Background — Despite clear and numerous benefits to prenatal detection of CHD and
an ability for fetal ultrasound to detect over 90% of CHD lesions in theory, in practice the fetal CHD detection
rate is closer to 50%. Prior literature suggests a key cause of this startling diagnosis gap is suboptimal acquisition
and interpretation of fetal heart images. DL is a novel data science technique that is proving excellent at pattern
recognition in images. DL models are a function of the design and tuning of a neural network architecture, and
the curation and processing of the image data used to train the network. Preliminary Studies — We have
assembled a multidisciplinary team of experts in echocardiography and CHD (Drs. Grady, Levine, and Arnaout),
DL and data science (Drs. Keiser, Butte and Arnaout), and statistics and clinical research (Drs. Arnaout and
Grady) and secured access to tens of thousands of multicenter (UCSF and six other centers), multimodal fetal
imaging studies. We have created a scalable image processing pipeline to transform clinical studies into image
data ready for computing. We have designed and trained DL models to find key cardiac views in fetal ultrasound,
calculate standard and advanced fetal cardiac biometrics from those views, and distinguish between normal
hearts and certain CHD lesions. Hypothesis — While DL is powerful, much work is still needed to adapt it for
clinical imaging and to translate it toward clinically relevant performance in patient populations. We hypothesize
that an integrated ensemble DL/ML approach can lead to vast improvements in fetal CHD diagnosis. Aims —
To this end, the main Aims of this proposal are (1) to develop and optimize neural network architectures and
efficient data inputs to relieve key performance bottlenecks for DL in fetal CHD; and (2) to deploy DL models
population-wide to evaluate their ability to improve diagnosis, biometric characterization, and precision
phenotyping over the current standard of care. Our methods include DL/ML algorithms and retrospective imaging
analysis. Environment and Impact — This work will be supported in an outstanding environment for research
at the crossroads of data science, cardiovascular and fetal imaging, and translational informatics. The work
proposed will provide valuable tools and insight into designing and evaluating both the data and the algorithms
for DL on...

## Key facts

- **NIH application ID:** 10608075
- **Project number:** 5R01HL150394-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Rima Arnaout
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $807,062
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10608075

## Citation

> US National Institutes of Health, RePORTER application 10608075, Improving cardiovascular image-based phenotyping using emerging methods in artificial intelligence (5R01HL150394-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10608075. Licensed CC0.

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