The projects aim to predict hospital readmissions in high-risk heart failure patients and establish reliable methods for evaluating the safety and performance of AI-enabled medical imaging systems.
Heng Huang, leader of applied AI at the University of Maryland Institute for Health Computing (UM-IHC), is part of two new federally funded projects aimed at improving the use of artificial intelligence in healthcare: a four-year, $1.2 million award to develop a wearable device that predicts hospital readmission in high-risk heart failure patients and a $750,000 contract to create a platform for evaluating AI-enabled medical imaging systems.
“AI has so much potential to help us address human health problems, and developing these kinds of real-world tools is one important way forward,” said Huang, who is also the Brendan Iribe Endowed Professor of Computer Science at the University of Maryland with a joint appointment in the UMD Institute for Advanced Computer Studies.
With the $1.2 million grant, funded jointly by the National Institutes of Health and the U.S. National Science Foundation, Huang will collaborate with Wei Gao at the University of Pittsburgh to develop and test a small, affordable wearable device equipped with AI. The device will use magnetic sensing and other sensors to monitor heart failure patients’ vital signs at home. New machine learning models will combine the data in real time to predict patients’ risk of hospital readmission.
“Heart failure is the leading cause of hospitalization in older adults, and hospital readmissions after discharge are common and have become the top reason for worse clinical outcomes,” said Huang. “The problem is especially serious among patients with obesity, who are 30% more likely to require rehospitalization than other heart failure patients.”
The models will enable the researchers to identify risk factors from patients’ everyday vital signs and important biomarkers and predict readmission before it occurs.
“Our ultimate goal is to prevent those hospital returns to help save lives,” Huang said.
Data collected by the device could also help the researchers better identify the mechanisms and symptoms of heart failure and support clinicians in making an initial diagnosis.
Assessing AI-enabled medical imaging
Huang is also collaborating with UM-IHC colleagues on a new Food and Drug Administration (FDA)-funded project to create a platform to evaluate AI-enabled medical imaging systems for detecting pulmonary embolism, a condition in which a blood clot blocks an artery.
“This project goes to the question of trustworthiness of clinical AI in patient care. If we’re going to use AI to review radiology scans, we need to be able to evaluate what it gets right and what the failure points are,” said lead awardee Florence Doo, an assistant professor in diagnostic radiology and nuclear medicine at the University of Maryland School of Medicine (UMSOM) who co-leads with Huang the AI-enabled medical imaging team in UM-IHC’s Center for Applied AI. “We are building a test bed, or dashboard, that will let clinicians and the FDA evaluate post-market performance and safety of AI in very precise ways, in a health system and at scale.”
The researchers will first standardize hospital imaging data, radiology reports and hospital reports for use in evaluating medical AI. Those reports will come from nearly 6,000 patient records from the University of Maryland Medical System as well as public datasets.
“We will then deliberately stress-test AI models to see where they fail and document what kinds of mistakes AI makes and under what circumstances,” Huang said.
The team also includes Melvin Sharoky MD Professor of Medicine at UMSOM Bradley Maron and UMD Computer Science Professor Adam Porter—the co-executive directors of the UM-IHC.
“It’s not enough to just validate an AI-enabled device on benchmark data at a single point in time; we must also validate that the device works correctly throughout its useful lifetime,” Porter added. “Therefore, post-market evaluation of AI devices is a key focus of this project.”
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This work is supported by the National Institutes of Health and the U.S. National Science Foundation (Award No. R01NR022545) and the FDA (Contract No. 75F40126C00094). This article does not necessarily reflect the views of these organizations.
Additional awardees on the FDA contract from the AI-Enabled Medical Imaging Team include Nikhil Shah, Amritansh Suryavanshi and Pravnav Kulkarni.
