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BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories.
J Chem Inf Model
January 2025
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
View Article and Find Full Text PDFUnified Mobile App for Streamlining Verbal Autopsy and Cause of Death Assignment in India: Design and Development Study.
JMIR Form Res
January 2025
ICMR-National Institute for Research in Digital Health and Data Science, Ansari Nagar, New Delhi, 110029, India, 91 7840870009.
Background: Verbal autopsy (VA) has been a crucial tool in ascertaining population-level cause of death (COD) estimates, specifically in countries where medical certification of COD is relatively limited. The World Health Organization has released an updated instrument (Verbal Autopsy Instrument 2022) that supports electronic data collection methods along with analytical software for assigning COD. This questionnaire encompasses the primary signs and symptoms associated with prevalent diseases across all age groups.
View Article and Find Full Text PDFInfarct core segmentation using U-Net in CT perfusion imaging: a feasibility study.
Acta Radiol
January 2025
Department of Medical Imaging, Dalin Tzu-Chi Hospital, Chiayi, Taiwan.
Background: The wide variability in thresholds on computed tomography (CT) perfusion parametric maps has led to controversy in the stroke imaging community about the most accurate measurement of core infarction.
Purpose: To investigate the feasibility of using U-Net to perform infarct core segmentation in CT perfusion imaging.
Material And Methods: CT perfusion parametric maps were the input of U-Net, while the ground truth segmentation was determined based on diffusion-weighted imaging (DWI).
3D MRI with CT-like bone contrast (3D-BONE): a pictorial review of clinical applications.
Acta Radiol
January 2025
Department of Radiology, Changi General Hospital, Singapore, Republic of Singapore.
Background: Computed tomography (CT) is the gold standard imaging modality for the assessment of 3D bony morphology but incurs the cost of ionizing radiation exposure. High-resolution 3D magnetic resonance imaging (MRI) with CT-like bone contrast (CLBC) may provide an alternative to CT in allowing complete evaluation of both bony and soft tissue structures with a single MRI examination.
Purpose: To review the technical aspects of an optimized stack-of-stars 3D gradient recalled echo pulse sequence method (3D-Bone) in generating 3D MR images with CLBC, and to present a pictorial review of the utility of 3D-Bone in the clinical assessment of common musculoskeletal conditions.
Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems.
Circ Genom Precis Med
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (A.A., L.S.D., E.K.O., R.K.).
Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.
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