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Purpose Of Review: There has been increasing use of multimodality imaging in the evaluation of cardiomyopathies.
Recent Findings: Echocardiography, cardiac magnetic resonance (CMR), cardiac nuclear imaging, and cardiac computed tomography (CCT) play an important role in the diagnosis, risk stratification, and management of patients with cardiomyopathies. Echocardiography is essential in the initial assessment of suspected cardiomyopathy, but a multimodality approach can improve diagnostics and management. CMR allows for accurate measurement of volumes and function, and can easily detect unique pathologic structures. In addition, contrast imaging and parametric mapping enable the characterization of tissue features such as scar, edema, infiltration, and deposition. In non-ischemic cardiomyopathies, metabolic and molecular nuclear imaging is used to diagnose rare but life-threatening conditions such amyloidosis and sarcoidosis. There is an expanding use of CCT for planning electrophysiology procedures such as cardioversion, ablations, and device placement. Furthermore, CCT can evaluate for complications associated with advanced heart failure therapies such as cardiac transplant and mechanical support devices. Innovations in multimodality cardiac imaging should lead to increased volumes and better outcomes.
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http://dx.doi.org/10.1007/s11886-024-02068-9 | DOI Listing |
Alzheimers Dement
September 2025
Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, China.
Cognitive impairment and dementia, including Alzheimer's disease (AD), pose a global health crisis, necessitating non-invasive biomarkers for early detection. This review highlights the retina, an accessible extension of the central nervous system (CNS), as a window to cerebral pathology through structural, functional, and molecular alterations. By synthesizing interdisciplinary evidence, we identify retinal biomarkers as promising tools for early diagnosis and risk stratification.
View Article and Find Full Text PDFHum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFPediatr Transplant
November 2025
Division of Urology, University of Toronto, Toronto, Canada.
Introduction: Differentiating acute tubular necrosis (ATN) from rejection in pediatric kidney transplant (KT) recipients remains challenging and necessitates invasive biopsy. Doppler ultrasound-derived resistive index (RI) is a noninvasive modality to assess graft status, but its diagnostic utility in children is unclear. This study evaluates RI's ability to distinguish ATN and rejection in KT.
View Article and Find Full Text PDFRetin Cases Brief Rep
September 2025
Retinal Disorders and Ophthalmic Genetics Division, Stein Eye Institute, University of California of Los Angeles, David Geffen School of Medicine at UCLA, Los Angeles, California, United States.
Purpose: To describe a case of recalcitrant bilateral peripapillary pachychoroid syndrome (PPS) treated with high-dose (HD) intravitreal aflibercept injections.
Methods: Medical and imaging records were retrospectively evaluated. Multimodal imaging included ultra-widefield indocyanine green and fluorescein angiography and fundus autofluorescence.
Front Digit Health
August 2025
Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.
Introduction: Vision language models (VLMs) combine image analysis capabilities with large language models (LLMs). Because of their multimodal capabilities, VLMs offer a clinical advantage over image classification models for the diagnosis of optic disc swelling by allowing a consideration of clinical context. In this study, we compare the performance of non-specialty-trained VLMs with different prompts in the classification of optic disc swelling on fundus photographs.
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