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Transmantle pressure under the influence of free breathing: non-invasive quantification of the aqueduct pressure gradient in healthy adults.
Fluids Barriers CNS
January 2025
Medical Image Processing Department, CHU Amiens-Picardie University Hospital, Amiens, France.
Background: The pressure gradient between the ventricles and the subarachnoid space (transmantle pressure) is crucial for understanding CSF circulation and the pathogenesis of certain neurodegenerative diseases. This pressure can be approximated by the pressure difference across the aqueduct (ΔP). Currently, no dedicated platform exists for quantifying ΔP, and no research has been conducted on the impact of breathing on ΔP.
View Article and Find Full Text PDFDeep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset.
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFNuclear Cardiology Surrogate Biomarkers in Clinical Trials.
J Nucl Med
January 2025
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California;
Nuclear cardiology offers a diverse range of imaging tools that provide valuable insights into myocardial perfusion, inflammation, metabolism, neuroregulation, thrombosis, and microcalcification. These techniques are crucial not only for diagnosing and managing cardiovascular conditions but also for gaining pathophysiologic insights. Surrogate biomarkers in nuclear cardiology, represented by detectable imaging changes, correlate with disease processes or therapeutic responses and can serve as endpoints in clinical trials when they demonstrate a clear link with these processes.
View Article and Find Full Text PDFThe Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.
JMIR Cardio
December 2024
School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
Background: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility.
View Article and Find Full Text PDFA novel fuzzy three-valued logic computational framework in machine learning for medicine dataset.
Comput Biol Med
January 2025
Department of Mathematics, NED University of Engineering & Technology, Pakistan. Electronic address:
For consideration of uncertainties of a medicine dataset, a new conceptual architecture fuzzy three-valued logic is introduced in this research work. The proposed concept is applied to the heart disease dataset for the assessment of heart disease risk in individuals. By comparison of three binary (0,1) input variables, the variables' uncertainties and their collective impact can be analyzed that provide complete information leading to better outcome prediction.
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