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Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.
Radiology
January 2025
From the Department of Radiology, University of Washington, UW Medical Center-Montlake, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC), University of Washington, Seattle, Wash (D.M.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (M.v.A.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.H.); Department of Radiology, Mayo Clinic, Rochester, Minn (T.L., E.E.W.); Departments of Cardiology and Radiology, Royal Brompton Hospital, London, United Kingdom (E.D.N.); School of Biomedical Engineering and Imaging Sciences, King's College, London, United Kingdom (E.D.N.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (B.D.A.); Department of Radiology, University of Cagliari, Cagliari, Italy (L.S.); Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1 Postbus 30 001, 9700 RB Groningen, the Netherlands (R.V.); Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.H.).
Artificial intelligence (AI) offers promising solutions for many steps of the cardiac imaging workflow, from patient and test selection through image acquisition, reconstruction, and interpretation, extending to prognostication and reporting. Despite the development of many cardiac imaging AI algorithms, AI tools are at various stages of development and face challenges for clinical implementation. This scientific statement, endorsed by several societies in the field, provides an overview of the current landscape and challenges of AI applications in cardiac CT and MRI.
View Article and Find Full Text PDFAdvancements in Artificial Intelligence in Noninvasive Cardiac Imaging: A Comprehensive Review.
Clin Cardiol
January 2025
Department of Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Background: Technological advancements in artificial intelligence (AI) are redefining cardiac imaging by providing advanced tools for analyzing complex health data. AI is increasingly applied across various imaging modalities, including echocardiography, magnetic resonance imaging (MRI), computed tomography (CT), and nuclear imaging, to enhance diagnostic workflows and improve patient outcomes.
Hypothesis: Integrating AI into cardiac imaging enhances image quality, accelerates processing times, and improves diagnostic accuracy, enabling timely and personalized interventions that lead to better health outcomes.
Long term outcome after surgical tetralogy of Fallot repair at young age: Longitudinal follow-up up to 50 years after surgery.
Int J Cardiol
January 2025
Department of Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, the Netherlands; European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart (ERN GUARD-Heart), Amsterdam, the Netherlands.
Background: Little is known about the very long-term outcome in Tetralogy of Fallot (ToF) patients.
Objectives: To prospectively evaluate clinical outcome and quality-of-life after surgical repair of ToF.
Methods: Single-centre, longitudinal cohort-study evaluating every decade 144 ToF patients who underwent surgical repair <15 years of age between 1968 and 1980.
Comparative analysis of left atrial size and appendage morphology in paroxysmal and persistent atrial fibrillation patients.
J Arrhythm
February 2025
Heart Center Munich-Bogenhausen, Department of Cardiology and Internal Intensive Care Medicine Munich Hospital Bogenhausen, Munich Municipal Hospital Group Munich Germany.
Purpose: Pulmonary vein isolation (PVI) is effective in treating atrial fibrillation (AF), but outcomes are worse for persistent AF (persAF) patients than paroxysmal AF (PAF) patients. The study aimed to identify differences in left atrial (LA) and left atrial appendage (LAA) anatomy in different AF types.
Methods: In a single-center observational study, a blinded retrospective analysis of preprocedural cardiac computed tomography angiography (CCTA) images was performed.
Endothelial TRIM35-Regulated MMP10 Release Exacerbates Calcification of Vascular Grafts.
Adv Sci (Weinh)
January 2025
Clinical Research Center, Postdoctoral Station of Clinical Medicine, The Third Xiangya Hospital, Central South University, Changsha, 410013, P. R. China.
Vascular calcification is a highly regulated process in cardiovascular disease (CVD) and is strongly correlated with morbidity and mortality, especially in the adverse stage of vascular remodeling after coronary artery bypass graft surgery (CABG). However, the pathogenesis of vascular graft calcification, particularly the role of endothelial-smooth muscle cell interaction, is still unclear. To test how ECs interact with SMCs in artery grafts, single-cell analysis of wild-type mice is first performed using an arterial isograft mouse model and found robust cytokine-mediated signaling pathway activation and SMC proliferation, together with upregulated endothelial tripartite motif 35 (TRIM35) expression.
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