CompHealth Corner June-July 2026
Computational and digital health ideas evolve quickly. Our monthly research roundup highlights the latest publications — from our Center faculty and other leaders in the field. Here’s what you should be reading this month!
High-frequency oscillations in intraoperative recordings from hybrid arrays with micro-and macrocontacts
Cecilia Schmitz, Katrina J Barth, Charles Wang, Zachary Spalding, Suseendrakumar Duraivel, Birgit Frauscher, Derek G Southwell, Gregory B Cogan, Justin Blanco and Jonathan Viventi. “High-frequency oscillations in intraoperative recordings from hybrid arrays with micro-and macrocontacts.” Journal of Neural Engineering (2026).
A new study published in the Journal of Neural Engineering by researchers including Duke Center for Computational and Digital Health Innovation members Jonathan Viventi and Gregory B. Cogan examined whether next-generation brain recording devices can better detect electrical activity associated with epilepsy.
Using hybrid electrode arrays that combined standard clinical electrodes with much smaller, high-density microelectrodes, the team recorded brain activity during epilepsy surgery. The microelectrodes detected 14 times more high-frequency oscillations (HFOs)—a promising biomarker of epileptic brain tissue—than conventional clinical electrodes covering the same area. Remarkably, 93% of the HFOs identified by the microelectrodes were missed by standard electrodes.
The findings suggest that many epilepsy-related signals originate from extremely small regions of cortex, often less than 1 mm in radius, making them difficult to capture with current clinical recording technologies.
By improving the detection and localization of these signals, high-density microelectrode arrays could help surgeons more precisely identify and target epileptic tissue in patients with drug-resistant epilepsy. The work highlights how advances in sensing technology and neural engineering may lead to more accurate surgical planning and improved outcomes for people living with epilepsy.
The need to develop health data transaction disclosure requirements to balance transparency, privacy, and progressive use
Matthew G Crowson MD MBI, Jade Z H Tan, Jessilyn Dunn PhD, Jay Bhatt MD, Zachary Schneider, Daniel Forger PhD, Leo A Celi MD MPH, Matilda Dorotic PhD, Prof Bradley Malin. “The need to develop health data transaction disclosure requirements to balance transparency, privacy, and progressive use.” The Lancet Digital Health (2026).
As artificial intelligence becomes increasingly integrated into health care, questions surrounding privacy, transparency, and data ownership are more important than ever.
This article in The Lancet Digital Health, co-authored by our Associate Director of Wearables Jessilyn Dunn, examines the growing commercialization of health data and the need for stronger safeguards around how patient information is shared, used, and monetized.
The authors call for clearer health data transaction disclosure requirements, patient-centered data practices, and regulatory frameworks that promote both innovation and accountability. As digital health technologies continue to evolve, building and maintaining public trust will be essential to ensuring that data-driven advances benefit patients while protecting privacy and individual rights.
At the Center for Computational and Digital Health Innovation, we believe these conversations are critical to shaping the future of responsible digital health.
High-throughput adaptive physics refinement for tissue-scale adhesive dynamics
Aristotle Martin, William Ladd, Runxin Wu, Amanda Randles. “High-throughput adaptive physics refinement for tissue-scale adhesive dynamics.” Journal of Computational Science (2026): 102812.
Understanding how circulating tumor cells interact with blood vessel walls is critical to understanding cancer metastasis, but capturing these interactions across realistic vascular networks at submicrometer resolution has historically required prohibitive computational resources.
A new publication from Center Director Amanda Randles and Center members Aristotle Martin, Ph.D., William Ladd, and Wendy Wu in the Journal of Computational Science introduces High-Throughput Adaptive Physics Refinement (APR-AD), a computational framework that dramatically expands the scale of adhesive transport simulations while maintaining the detailed physics needed to model receptor-mediated interactions.
By combining adaptive physics refinement with hybrid CPU-GPU computing and scalable parallel algorithms, the platform can simulate more than 3,000 circulating tumor cells simultaneously while reducing memory requirements by approximately 15× compared to fully explicit models. These advances transform APR-AD into a powerful computational microscope for studying cancer transport and other biological processes that span multiple length scales.
At the Center for Computational and Digital Health Innovation, we’re proud to see our researchers continue pushing the boundaries of computational science, enabling simulations that bring us closer to understanding—and ultimately combating—complex diseases like cancer.
Predictors of ischemic stroke and major bleeding among patients with atrial fibrillation in clinical practice
Pishoy Gouda, MBBCh, BAO, MSc, Josephine Harrington, MD, Kelly Arps, MD, Gretchen Sanders, RN, Anqi Chen, Msc, Karen Chiswell, PhD, Paul Hofmann, BS, Keith Marsolo, PhD, Kirubel Asfaw, MSc, Rosa Coppolecchia, DO, John H. Alexander, MD, MHS, Jonathan Piccini, MD, Christopher B. Granger, MD, Heidi T. May, MD, Elizabeth Chrischilles, MD, Benjamin A. Steinberg, MD, Alanna M. Chamberlain, MD, Jeffrey VanWormer, MD, William Schuyler Jones, MD, Manesh R. Patel, MD. “Predictors of ischemic stroke and major bleeding among patients with atrial fibrillation in clinical practice.” American Heart Journal (2026): 107490.
A new study published in the American Heart Journal by researchers including Center investigators Schuyler Jones and Manesh Patel, MD, examined whether routinely available clinical data can improve prediction of ischemic stroke and major bleeding in patients with atrial fibrillation (AF) beyond today’s standard risk scores.
Using electronic health record data from more than 214,000 patients across Duke University Health System and the PCORnet network, the team evaluated how well the widely used CHA₂DS₂-VASc score predicts stroke risk in patients receiving different anticoagulation strategies. They found that the traditional score provided only modest predictive performance, but incorporating additional routinely collected clinical information substantially improved prediction of both ischemic stroke and major bleeding.
Importantly, factors such as prior major bleeding, kidney function, hemoglobin levels, aspirin use, and demographic variables captured meaningful sources of residual risk that are not included in current clinical scoring systems. The findings suggest that electronic health records can support more personalized and comprehensive risk assessment than simple bedside calculators alone.
As healthcare increasingly leverages computational methods and large-scale clinical data, studies like this demonstrate how data-driven risk models can move beyond one-size-fits-all approaches to support more individualized treatment decisions for patients with atrial fibrillation.
