# Deep Learning for Pulmonary Embolism Imaging Decision Support: A Multi-institutional Collaboration

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $345,325

## Abstract

Project Summary
Diagnostic imaging costs $100 billion annually. These healthcare costs are expected to increase in the coming
decade as the national population ages and the pool of insured patients increases. The size and growth of these
costs concern policy makers, payers, and society alike. The use of advanced imaging for PE has increased 27
fold in recent years, and this sharp escalation has the potential to expose patients to unnecessary procedures,
tests, and risks due to incidental findings. Although radiologists do not order most radiology exams, these
physicians are the target of criticism about the rising costs and possible overuse of radiology services. The
healthcare industry has called upon radiologists to manage the potential overuse of advanced imaging and to
take the lead on investigating best practices for the optimal use of advanced imaging.
The ideal sources of information for imaging utilization guidelines are randomized, controlled imaging clinical
trials. However, these trials are cost and time intensive, exceedingly difficult to conduct, and typically use narrow
patient-inclusion criteria, making it challenging to generalize the results to broader clinical situations. Alternative
sources of reliable evidence, such as observational or retrospective studies, have been lacking. The widespread
adoption of electronic medical records (EMRs) and the increasing availability of computational methods to
process vast amounts of unstructured information now make it possible to learn directly from practice-based
evidence. We propose that “big data” clinical repositories, including radiology reports, can lend themselves to a
treasure trove of point-of-care, relevant, actionable data that can be used in an innovative and cost-sensitive
approach to evaluate the appropriate use of medical imaging. We aim to create a predictive model that
leverages real-time EMR clinical data from top national medical centers to arrive at a patient-specific
imaging outcome prediction. We recognize that clinicians have to make on-the-spot medical imaging-ordering
decisions and they generally do not comply with existing clinical decision support rules. Our study aims to provide
clinicians with a tool that can leverage aggregate patient data for medical imaging decision making at the point
of care.
The overarching approach of this study is to utilize scalable methodology that can be widely applied to
leverage EMR data to predict the outcome of a several other high-cost, low-yield imaging tests. This
proposal has the potential to better inform advanced imaging in the learning healthcare system of the future and
reduce unnecessary imaging examinations and healthcare costs.

## Key facts

- **NIH application ID:** 10165820
- **Project number:** 5R01LM012966-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** NIGAM H SHAH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $345,325
- **Award type:** 5
- **Project period:** 2018-09-11 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10165820

## Citation

> US National Institutes of Health, RePORTER application 10165820, Deep Learning for Pulmonary Embolism Imaging Decision Support: A Multi-institutional Collaboration (5R01LM012966-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10165820. Licensed CC0.

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