# Computer-Aided Triage of Body CT Scans with Deep Learning

> **NIH NIH R01** · DUKE UNIVERSITY · 2024 · $552,589

## Abstract

PROJECT SUMMARY / ABSTRACT
Computed tomography (CT) imaging for the body can result in thousands of images spanning many organs
and myriad possible diseases. With growing patient load as well as increasing resolution and complexity of
scans, the task of CT interpretation has become daunting. To improve radiologist performance, many artificial
intelligence (AI) algorithms have been produced, but most are limited by their very narrow application to a
specific disease in a specific organ or have been trained on limited data due to the high cost and complexity of
manual annotation. As a result, there is an unmet need because existing AI solutions have not significantly
improved the workflow or performance of radiologists.
To meet these needs, we propose to develop a computer-aided diagnosis triage tool for CT of the chest,
abdomen, and pelvis (CAP) that would focus radiologists’ attention on regions with a high likelihood of
actionable disease while minimizing search efforts in regions of low likelihood.
Our hypothesis is that a triage tool will improve radiologist workflow while simultaneously maintaining or
improving performance. Our long-term goal is to create a clinical decision support system that will address
bottlenecks of radiologist workflow and performance. As key steps toward demonstrating feasibility for that
goal, we propose the following three specific aims:
1. Create framework for the assembly, deidentification, annotation, and sharing of over a million chest,
 abdomen, pelvis (CAP) CT cases from two major health systems.
2. Develop a triage system trained using multi-site CT datasets through collaborative/federated learning.
3. Pilot use of the triage system at multiple sites to allow radiologists to perform efficiently and equivalently for
 clinical tasks of assessing actionable disease in CAP CT.
Key innovations will include the use of weak supervision to label a massive number of cases from two health
systems. Labeling will be based on rule-based expert systems as well as natural language processing. Image
classification will be based on deep learning models capable of processing an entire 3D CT volume and trained
with federated learning to leverage the rich heterogeneity of data from the two health systems.
The expected outcome of this project will be evidence to support a new clinical workflow for radiologist
interpretation, which is the foundation for all medical imaging. For this project, we will maximize impact by
addressing CAP CT because of the large patient load and complex anatomy/disease, and by producing one of
the largest medical imaging datasets that can be shared for future research including grand challenges. In
addition, by leveraging existing data in patient archives and radiology reports, our approach has the potential to
be applicable to other body sites or imaging modalities in the future.

## Key facts

- **NIH application ID:** 10908263
- **Project number:** 5R01CA261457-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** JOSEPH Y LO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $552,589
- **Award type:** 5
- **Project period:** 2023-08-16 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10908263

## Citation

> US National Institutes of Health, RePORTER application 10908263, Computer-Aided Triage of Body CT Scans with Deep Learning (5R01CA261457-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10908263. Licensed CC0.

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