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When analyzed by a new artificial intelligence model developed at Penn Medicine, CT scans reveal much about a patient's health beyond the specific condition they were originally ordered for, according to a new study published in npj Digital Medicine. The AI model, dubbed Percival, can uncover nearly hidden patterns in disease and potential future health risks that might have otherwise gone unnoticed by human eyes. While other models may need to be directed to find certain health conditions or outcomes, Percival can run in the background to pick up what is unseen.

"Our teams have developed something that learns broadly from imaging data and uncovers connections between scans, lab values, people's physiology and clinical outcomes to potentially give more accurate assessments of health, or even ring alarm bells early," said senior author Walter Witschey, an associate professor of radiology and the associate vice chair of research IT. "This is work that points toward a future in which medical imaging becomes a foundation for more personalized and proactive health care."

Named as a reference to one of the knights of King Arthur's Round Table, and also in reverence for the late Eugene Percival Pendergrass, a prominent radiologist and educator at Penn, Percival was built with help from a variety of teams at Penn Medicine and trained on data from the Penn Medicine BioBank. With information from more than 350,000 people collected across the University of Pennsylvania Health System over the course of two decades, the BioBank provides a diverse source of data for a model like Percival to be built upon.

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