# Population-level Pulmonary Embolism Outcome Prediction with Imaging and Clinical Data: A Multi-Center Study

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $310,600

## Abstract

Project Summary
The goal of the parent award is to develop an automated healthcare AI (AI-HC) to achieve point-of-care risk
stratification for pulmonary embolism (PE) patient outcomes using a fusion deep learning strategy that can
simultaneously analyze health records and imaging data. An ideal PE risk-scoring system would not only
predict mortality, but also assess the risk for the many debilitating long-term consequences of acute PE. Such
a system would, therefore, facilitate optimal management and would likely require intelligent use of clinical,
laboratory, and imaging data together in order to provide accurate patient -specific risk scoring for multiple PE
outcome measures. In order to build a robust model, the parent award will apply distributed training of deep
learning models across four large US healthcare institutions. Distributing the algorithm rather than the data
avoids sharing individually identifiable patient information. If successful, this parent award will be the first
endeavor to leverage diagnostic imaging (pixel) data in combination with structured and unstructured electronic
medical record (EMR) data to predict outcomes. Using a powerful combination of clinical, laboratory, and
imaging data, this system will provide patient-specific risk scoring for multiple PE outcome measures. Further,
the parent award fosters multi- center collaborations, including investigation of the generalizability of the
approach to different populations of PE patients and to train, test, and ultimately deploy the automated
predictive model in a variety of clinical environments.
Partnering with the parent award presents a unique opportunity to address two pressing ethical questions: How
do you anticipate, identify, and address ethical problems with AI-HC before they cause harm? How do you
document and communicate important ethical constraints with AI-HC, once identified, to multiple users
(including the developers of the AI)? The supplement team has worked closely with the parent award
investigators on ethics of AI-HC generally and on developing approaches to examine AI-HC. In this
supplement we will pilot an approach to: 1) identify ethical issues that may emerge with development and
multi-site deployment of AI-HC for PE; and 2) develop consensus on how to address these ethical issues. We
will also 3) develop consensus on an ethics “label” to communicate identified and addressed ethical
constraints. In doing 1, 2 & 3 we will refine a generalizable approach for identifying and addressing ethical
challenges with an AI-HC and a roadmap for how to communicate identified ethical concerns for AI-HC.

## Key facts

- **NIH application ID:** 10598324
- **Project number:** 3R01HL155410-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** CURTIS P LANGLOTZ
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $310,600
- **Award type:** 3
- **Project period:** 2022-09-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10598324

## Citation

> US National Institutes of Health, RePORTER application 10598324, Population-level Pulmonary Embolism Outcome Prediction with Imaging and Clinical Data: A Multi-Center Study (3R01HL155410-02S1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10598324. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*