Guiding Principles

Real-Time Peripheral Revascularization Planning in Chronic Limb Threatening Ischemia Using HarVI: A Digital Twin Approach

Peripheral artery disease (PAD) is a leading cause of limb loss and morbidity worldwide, with chronic limb-threatening ischemia (CLTI) representing its most severe presentation. Although image-guided endovascular interventions are routinely performed, clinicians currently lack tools that provide real-time, patient-specific predictions of hemodynamic outcomes to guide revascularization decisions. Existing computational fluid dynamics (CFD) approaches can recover…

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Arriel Benis

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Diabetes Self-Management in the Digital Health Era: A Concept Analysis Using Natural Language Processing

Diabetes self-management is increasingly shaped by digital health technologies, yet existing conceptual definitions do not fully reflect this evolving landscape. This study aimed to refine the concept of diabetes self-management in the digital health era by applying Rodgers’ evolutionary method integrated with Natural Language Processing (NLP). Ninety-seven studies were analyzed through a dual-phase design combining…

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Development of a Pipeline for Transitioning Wearable and Non-Wearable Data to a Centralized Storage, Analysis and Downstream Applied Research Platform

The Luck Lab developed an integrated data framework that links youth athletes’ reported activity and exposure with wearable head-impact kinematics to improve the ability to find, track, and treat mild traumatic brain injury.

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A Centralized Virtual Care Support Model for Improved Telehealth Operational Outcomes

As telehealth achieves a steady state in the post–Covid-19 pandemic health care landscape, health systems require operational models that can support clinicians across service lines. The authors describe a highly successful case study of a centralized telehealth support model, the Virtual Care Center (VCC), within Duke University Health System, a large academic health system. Duke…

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AI Engineers Nanoparticles for Improved Drug Delivery
Posted onSeptember 26, 2025September 26, 2025

Biomedical engineers at Duke University, including Center member Daniel Reker, have developed a platform that combines automated wet lab techniques with artificial intelligence (AI) to design nanoparticles for drug delivery. The approach could help researchers deliver difficult-to-encapsulate therapeutics more efficiently and effectively. Read the article

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Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning

Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visits. Although predictions made at later stages typically achieve higher precision, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on improving prediction…

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Digital twins for noninvasively measuring predictive markers of right heart failure

Digital twins offer a promising approach to advancing healthcare by providing precise, noninvasive monitoring and early detection of diseases. In heart failure (HF), a leading cause of mortality worldwide, they can improve patient monitoring and clinical outcomes by simulating hemodynamic changes indicative of worsening HF. Current techniques are limited by their invasiveness and lack of…

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Physical artificial intelligence in nursing: Robotics

Robotics, driven by advancements in physical artificial intelligence (AI), offers potential solutions–yet many challenges– to creating innovative care models to meet the needs of the future. Purpose To present an overview of robotics across various industries and explain how physical AI is aiding the development and integration of robots into skilled nursing. We discuss the…

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Blood Flow Animation

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