Atrial fibrillation (AF) is the most common arrhythmia worldwide and is associated with significant morbidity and mortality. FibriCheck is a medical analysis platform that uses an end-to-end algorithm to detect AF based on photoplethysmography…
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Professor Yang Wei of the Nottingham School of Art & Design. Picture provided by Nottingham Trent University
Researchers are developing a smart textiles bra to help women with intellectual disabilities monitor for breast…
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Background: Cardiovascular diseases remain the leading cause of mortality worldwide, accounting for 18 million deaths annually. Detection and prediction of cardiovascular conditions are essential for timely intervention and improved patient outcomes. Wearable devices offer…
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Chris Wood, CEO of Reveal Dx
With lung cancer being the leading cause of cancer-related deaths worldwide, advancements in AI-powered healthcare technology are offering newfound hope. Over the span of the next few decades, AI…
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Sepsis is the leading cause of mortality in burn patients, yet early identification remains difficult due to persistent hyperinflammatory responses and altered baseline physiology. We developed a streamlined machine learning model for early sepsis…
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Michael Oleksiw, CEO of Pleio
Connectivity was supposed to bring us closer. Instead, it’s exposing a dangerous gap in our healthcare system. The very tools designed to link us have scaled rapidly, but without addressing…
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PreOpNet is a novel deep-learning algorithm using 12-lead digital electrocardiogram (ECG) for preoperative risk assessment of all-cause death and major adverse cardiac events (MACE) within 30 days. Its performance in European high-risk patients undergoing…
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Background: Surgical procedures and their potential complications place substantial strain on patients, clinicians, and health care systems. These strains are driven by the anticipated morbidity and mortality, so that there is resource-intensive postoperative inpatient…
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Carotid atherosclerosis is a key predictor of cardiovascular disease (CVD), necessitating early detection. While foundation models (FMs) show promise in medical imaging, their optimal selection and fine-tuning strategies for classifying carotid atherosclerosis from retinal…
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Predicting multiple postoperative complications remains challenging in perioperative care. Current approaches often address complications individually, limiting the potential for integrated risk assessment. We developed and externally validated a scalable, interpretable, tree-based multitask learning model…
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