# Clinical Decision Support for Assessing Pulmonary Embolism using Machine Learning

> **NIH NIH R44** · MINNESOTA HEALTHSOLUTIONS CORPORATION · 2022 · $1,018,633

## Abstract

Project Summary/Abstract
Minnesota HealthSolutions (MHS) proposes a Phase II project to develop and validate a software product
capable of automatically detecting and staging pulmonary embolisms (PEs) using clinically routine pulmonary
CT angiograms (CTAs). The proposed system will combine state-of-the-art machine learning methods and the
clinical expertise at Duke University into a system that integrates seamlessly into the Radiology workflow and
standard patient care path to improve the treatment decisions of physicians in the emergency department.
Pulmonary embolism is the third most common cause of death in hospital patients with an estimated
incidence of 1 per 1,000 patients. CTAs are routinely used to detect PE today; however, there is significant
variability in the detection rate among radiologists using CTA. Furthermore, despite the strong evidence that
the RV/LV ratio is an important clinical biomarker it is rarely measured quantitatively in practice. A successful
completion of this project would provide a workflow-integrated tool capable of faster PE detection and more
accurate staging of right heart strain to guide the physician’s treatment decision.

## Key facts

- **NIH application ID:** 10381875
- **Project number:** 2R44HL152825-02
- **Recipient organization:** MINNESOTA HEALTHSOLUTIONS CORPORATION
- **Principal Investigator:** Kevin M. Kramer
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,018,633
- **Award type:** 2
- **Project period:** 2022-02-17 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10381875

## Citation

> US National Institutes of Health, RePORTER application 10381875, Clinical Decision Support for Assessing Pulmonary Embolism using Machine Learning (2R44HL152825-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10381875. Licensed CC0.

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