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Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Investigating Cardiac Amyloidosis: A Primer for Clinicians.
R I Med J (2013)
February 2025
Department of Medicine, Division of Cardiology, Alpert Medical School of Brown University, Providence RI.
Cardiac amyloidosis (CA) is an infiltrative disease that results from the deposition of amyloid fibrils in the myocardium, resulting in restrictive cardiomyopathy. The amyloid fibrils are predominantly derived from two parent proteins, immunoglobulin light chain (AL) and transthyretin (ATTR), and ATTR is further classified into hereditary (ATTRv) and wild-type (ATTRwt) based on the presence or absence, respectively, of a mutation in the transthyretin gene. Once thought to be a rare entity, CA is increasingly recognized as a significant cause of heart failure due to improved clinical awareness and better diagnostic imaging.
View Article and Find Full Text PDFInfective endocarditis involving all native heart valves diagnosed by 18F-FDG PET.
Kardiol Pol
January 2025
Department of Cardiology, Medical University of Silesia, Katowice, Poland.
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.
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