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# Echocardiography

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  1. researchsquare.com

    Sepsis cardiomyopathy read from whole heartbeats: aligned cycles lift external AUROC to 0.929

    Sepsis-induced cardiomyopathy is heterogeneous and lacks agreed diagnostic criteria, so conventional echo measures and troponin capture only part of the dynamic change. A physiology-informed model cuts apical four-chamber loops into end-diastole-to-end-diastole cycles, derives left-ventricular area features, and fuses them with EchoJEPA visual representations and phase-aware attention before aggregating beats to a patient-level probability. In a 93-patient MIMIC-IV-ECHO external cohort it reached an AUROC of 0.929, against 0.735 for direct EchoJEPA classification, a paired difference of 0.194.

    Xuzhou Medical CollegeJiangsu Province HospitalNanjing Medical University
    Yuhan Zhao（First author）Yi Han（Corresponding author）
    Physiology-informed deep-learning frameworkEchoJEPASepsis-induced cardiomyopathy identificationEchocardiography
  2. European Heart Journal - Cardiovascular Imaging

    The added value of echocardiography in pulmonary arterial hypertension risk assessment: an artificial intelligence machine learning-derived analysis of the ULTRA RIGHT VALUE registry

    In a 401-patient multicentre European prospective pulmonary arterial hypertension (PAH) cohort, adding right ventricular echocardiographic parameters—especially right ventricular-pulmonary arterial (RV-PA) coupling indices—to the ESC/ERS and REVEAL 2.0 risk scores improved the c-index for morbi-mortality prediction from 0.73 to 0.78 and from 0.74 to 0.79, respectively (P<0.01), with machine learning identifying RV-PA coupling as the most influential dimension.

    Azienda sanitaria universitaria Giuliano IsontinaVrije Universiteit BrusselPoliclinico Umberto IJohn Paul II HospitalAHEPA University HospitalUniversité Libre de BruxellesSapienza University of RomeAristotle University of Thessaloniki
    Khodr Tello（First author）
    EchocardiographyPulmonary arterial hypertensionPAH risk score improvement
  3. International Journal of Cardiology

    AI-assisted handheld echocardiography for hospital bedside cardiac triage: the prospective OPTIMUST implementation study

    OPTIMUST was a prospective, single-center implementation study in which physicians without formal echocardiography certification completed a structured two-month curriculum and performed focused examinations using Caption AI-enabled handheld ultrasound on cardiology and non-cardiology wards; of 287 attempted examinations, 206 (71.8%) were analyzable, operator assessment correlated with expert review of the same handheld image sets for LVEF (r = 0.84), filling-pressure category agreement gave a quadratic weighted κ of 0.659, physicians reported that handheld findings changed or confirmed management in 93.7% of examinations, and 32.8% underwent comprehensive echocardiography within one month.

    Université de RennesInsermCentre Hospitalier Universitaire de Rennes
    Mathieu Paineau（First author）Erwan Donal（Corresponding author）
    EchocardiographyHandheld ultrasound deviceCaption AIOPTIMUST implementation study

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