Research using finger-clip sensors to predict breathing problems in premature babies and a machine learning-assisted wearable biosensor for multiplexed biomarker monitoring to manage gout flares are among the first pilot projects to be funded through the Riccio Digital Medicine and Health Technology Initiative, a collaboration between the University of Massachusetts Amherst and UMass Chan Medical School.

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UMass Amherst, the Riccio College of Engineering, and UMass Chan are providing funding for this year’s four awarded pilot projects, each receiving $100,000 for a 12-month project term. It is expected that these projects will lead to deeper research collaborations between the campuses and follow-on funding from external sources, such as the U.S. National Institutes of Health.

As part of his historic $50 million donation to the university in 2025 to name the college of engineering, Daniel J. Riccio ’86, ’24MS, designated that a portion of his gift provide seed funding to catalyze new initiatives, including this initiative on digital health research conducted by teams comprised of scientists from both campuses.

“My investment in the Riccio College of Engineering and UMass was inspired in part by the belief that combining the strengths of UMass Amherst and UMass Chan Medical School could accelerate the development of innovative digital health technologies that improve patient care and make a meaningful impact on the common good,” said Riccio. “These inaugural pilot projects demonstrate what’s possible when we bring talented researchers together across disciplines and campuses. I’m excited to see where these collaborations lead and the impact they can have.”

“These projects represent a terrific collaborative portfolio in digital medicine spanning chronic disease management, population health, metabolic health and disease prediction, with clear potential impacts on human health,” said Mike Malone, UMass Amherst vice chancellor of research and engagement.

“The goal of the initiative is to leverage the strengths of our multicampus system to become a driver for where digital technologies are heading,” said Govind Srimathveeravalli, director for the UMass Amherst Center for Personalized Health Monitoring. “As the share of senior citizens in our commonwealth grows, it is important they are provided accessible and quality healthcare so they may age in place with their families. Digital health technologies are central to this vision, where the engineers from UMass Amherst and the clinicians from UMass Chan are actively collaborating to advance digital health technologies.”

“We’re grateful to Mr. Riccio for his philanthropic support of UMass,” said David D. McManus, Chan ’02M.D., Chan ’12Sc.M., MBA, Chancellor of UMass Chan Medical School. “The initiative will enhance the partnership between UMass Chan and UMass Amherst and will lead to a deeper understanding of how digital healthcare can improve human lives.” 

The four pilot projects are:

A Machine Learning-Assisted Wearable Biosensor for Multiplexed Biomarker Monitoring

Led by Nianqiang Wu, distinguished professor of chemical and biomolecular engineering in the Riccio College of Engineering at UMass Amherst, co-PI Chao Zhao, associate professor of chemical and biomolecular engineering, and Timothy McAlindon, M.D., M.P.H., professor of medicine at UMass Chan

Management of gout—a painful type of arthritis caused by a buildup of uric acid crystals in the joints—depends on timely monitoring of both urine and the kidneys through intermittent clinic-based blood tests, which cannot capture dynamic biomarker changes during daily activities, acute flares and recovery phases. This project will develop a wearable sensor platform for stage-specific monitoring of metabolic waste products, enabling personalized management of gout.

Leveraging AI in OCT/OCTA Ocular Images to Determine Biomarkers of Progression to Advanced Age-Related Macular Degeneration and Apply to Clinical Practice

Led by Joyita Dutta, professor of biomedical engineering in the Riccio College of Engineering at UMass Amherst and Johanna M. Seddon, M.D., Sc.M., FARVO, FASRS, the Dr. Eileen L. Berman Foundation Chair in Biomedical Research II and professor of ophthalmology & visual sciences at UMass Chan

People with early stages of age-related macular degeneration (AMD) often have normal vision, making it difficult to predict, before vision loss occurs, who will progress to more advanced stages and blindness. Optical coherence tomography (OCT) and OCT angiography (OCTA), used to diagnose and monitor many retinal diseases including AMD, is a time-consuming and tedious process for clinicians. This research will use AI to incorporate OCT/COTA images and baseline clinical, demographic and lifestyle features to predict progression from non-advanced AMD to advanced AMD.

Development and Feasibility Testing of Pulse Oximetry Derived Digital Physiologic Biomarkers to Predict Respiratory Deterioration in Infants with Bronchopulmonary Dysplasia

Led by Hui Guan, assistant professor in the Manning College of Information and Computer Sciences at UMass Amherst and Lawrence M. Rhein, M.D., M.P.H., the Stoddard Chair of Pediatrics and professor of pediatrics at UMass Chan

Twenty-one to thirty-five percent of prematurely born infants are affected by a chronic lung condition called bronchopulmonary dysplasia (BPD), which can result in high rates of rehospitalization. Drs. Guan and Rhein want to see if they can use data from a pulse oximeter—the little clip placed on a patient’s finger or toe to measure oxygen levels—to predict when a baby’s breathing is about to get worse. They are focusing on infants with bronchopulmonary dysplasia (BPD), a chronic lung condition that mainly affects premature infants who need prolonged oxygen therapy or breathing machine support. Standard settings can produce a median of approximately 23 alarms per eight hours in infants with BPD, which results in alarm fatigue and missed escalation signals. This 12-month pilot will determine if pulse oximeter data can produce reliable digital biomarkers and preliminary models to predict when infants’ breathing may worsen.

Ring-Based Microcuff Blood Pressure Sensing to Identify Digital Biomarkers of Lupus Flare

Led by VP Nguyen, assistant professor in the Manning College of Information and Computer Sciences at UMass Amherst and Apurv Soni, M.D., Chan ’21Ph.D., assistant professor of medicine and director of the Program in Digital Medicine at UMass Chan 

Lupus is an autoimmune condition marked by periods of low disease activity interrupted by flares of organ damage. Researchers suspect that a flare may begin before a patient or physician is able to observe it, and existing monitoring tools weren’t designed to capture subtle changes that occur at home. The researchers will evaluate if a wearable, ring-based blood pressure monitor can track ongoing changes in blood pressure at night that are clinically relevant to predicting a lupus flare.