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A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
View Article and Find Full Text PDFRecruitment of Adolescents to Virtual Clinical Trials: Recruitment Results From the Health4Me Randomized Controlled Trial.
JMIR Pediatr Parent
December 2024
see Acknowledgments.
Background: Preventive interventions are needed to provide targeted health support to adolescents to improve health behaviors. Engaging adolescents in preventive interventions remains a challenge, highlighting the need for innovative recruitment strategies. Given adolescents' lives are intertwined with digital technologies, attention should be focused on these avenues for recruitment.
View Article and Find Full Text PDFUsing group model building to frame the commercial determinants of dietary behaviour in adolescence - findings from online system mapping workshops with adolescents, policymakers and public health practitioners in the Southwest of England.
BMC Public Health
January 2025
National Institute for Health Research (NIHR) School for Public Health Research (SPHR), Newcastle, UK.
Background: In England, 23% of children aged 11 start their teenage years living with obesity. An adolescent living with obesity is five times more likely to live with obesity in adult life. There is limited research and policy incorporating adolescents' views on how they experience the commercial determinants of dietary behaviour and obesity, which misses an opportunity to improve services and policies that aim to influence the prevalence of childhood obesity.
View Article and Find Full Text PDFClinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders.
J Med Internet Res
January 2025
Knight Foundation of Computing & Information Sciences, Florida International University, Miami, FL, United States.
Background: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech.
View Article and Find Full Text PDFMature-Aged People in the Rural Health Workforce System: A Systems Modelling Approach.
Aust J Rural Health
February 2025
Primary Care Connect, Shepparton, Australia.
Objective: The median age of people in rural areas is older than those living in metropolitan areas. Harnessing the potential of the mature-aged population in rural communities may present a uniquely sustainable approach to strengthening the rural health workforce system. The objective of this study was to map the rural health workforce system in Australia and identify the current and potential role of mature-aged people in the workforce system.
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