# Developing FAIR practices for cloud-enabled AI deployment for prospective testing

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $241,704

## Abstract

Supplemental Project Summary
Objective — The goal of the parent proposal is to develop and optimize novel deep learning (DL) approaches
to improve detection of congenital heart disease (CHD). We are using DL and related methods to extract
diagnosis, biometric characterizations, and other information from fetal ultrasound imaging. Notably, this work
includes retrospective evaluation in an imaging collection spanning two decades, tens of thousands of patients,
and several clinical centers across a range of healthcare settings. Background — Despite clear 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 is closer to 50%. Prior literature suggests a key cause of this startling
diagnosis gap is suboptimal acquisition and interpretation of fetal heart images. Preliminary Studies — Our
multi-disciplinary team in CHD and data science has successfully used DL to distinguish normal hearts from
those with complex CHD with an AUC of 0.99. Further retrospective validation shows our model to be an
anomaly detector appropriate for screening and has generated novel insights into study quality and
completeness that can improve clinical guidelines. The next logical step is prospective testing with a workflow
robust enough for deployment in the community. We have developed clinical feasibility testing partners for
cloud deployment, performed technical testing, and secured institutional approvals. Goals of Supplement —
Prospective multi-center testing for DL algorithms seems well-suited to cloud deployment. However,
determining how best to optimize cloud platforms with researchers, clinicians, and prospective testing in mind
is an open question. Whether the cloud can be used to enable integration with, or testing aboard, end-user
medical devices (‘edge devices’) is also unclear. Our goal in this supplement is to test these approaches.
Aims — (1) to develop a workflow for hosting deep learning models on the cloud (users can send data to the
cloud for inference directly from edge devices or via e.g. web application). Importantly, our approach is
Findable, Available, Interoperable, and Reusable (FAIR), by leveraging tools offered across cloud service
providers; being open-source, well-documented and well-covered by unit tests; being version-controlled and
complete (“containerized”); and being user-friendly for biomedical researchers and clinical partners to use and
re-use for different DL models. (2) We will perform feasibility testing in community fetal ultrasound clinics.
Environment and Impact — This work is supported in an outstanding environment at the crossroads of data
science, cardiovascular and fetal imaging, and translational informatics. Our testing partners span healthcare
settings and the world, forcing our workflows to be robust. We will publish our workflow, software container,
and documentation for the research community, as well as workflow and best...

## Key facts

- **NIH application ID:** 10827803
- **Project number:** 3R01HL150394-04S2
- **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:** $241,704
- **Award type:** 3
- **Project period:** 2023-09-08 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10827803

## Citation

> US National Institutes of Health, RePORTER application 10827803, Developing FAIR practices for cloud-enabled AI deployment for prospective testing (3R01HL150394-04S2). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10827803. Licensed CC0.

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