# Automated acute pulmonary embolism detection and staging from CT

> **NIH NIH R43** · MINNESOTA HEALTHSOLUTIONS CORPORATION · 2020 · $291,369

## Abstract

Project Summary / Abstract
Minnesota HealthSolutions (MHS) proposes a Phase I 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 of MEDNAX Radiology 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:** 10008093
- **Project number:** 1R43HL152825-01
- **Recipient organization:** MINNESOTA HEALTHSOLUTIONS CORPORATION
- **Principal Investigator:** Ryan M Chamberlain
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $291,369
- **Award type:** 1
- **Project period:** 2020-05-20 → 2022-01-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10008093

## Citation

> US National Institutes of Health, RePORTER application 10008093, Automated acute pulmonary embolism detection and staging from CT (1R43HL152825-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10008093. Licensed CC0.

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