OBJECTIVE: The aim of the study was to compare the diagnostic quality of deep learning (DL) reconstructed balanced steady-state free precession (bSSFP) single-shot (SSH) cine images with standard, multishot (also: segmented) bSSFP cine (standard cine...
Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care...
BACKGROUND: Amyloid PET/CT is essential for quantifying amyloid-beta (Aβ) deposition in Alzheimer's disease (AD), with the Centiloid (CL) scale standardizing measurements across imaging centers. However, MRI-based CL pipelines face challenges: high c...
INTRODUCTION: Quantification of amyloid plaques (A), neurofibrillary tangles (T), and neurodegeneration (N) using PET and MRI is critical for Alzheimer's disease (AD) diagnosis and prognosis. Existing pipelines face limitations regarding processing t...
OBJECTIVE: Cardiometabolic multimorbidity (CMM) significantly contributes to the economic burden in China, particularly in rural areas. This study aimed to analyze the spatiotemporal distribution of CMM and identify its primary influencing factors in...
BACKGROUND: Patient satisfaction after one year of distal radius fracture fixation is influenced by various aspects such as the surgical approach, the patient's physical functioning, and psychological factors. Hence, a multimodal machine learning pre...
BACKGROUND: Housing and income are important social determinants of health (SDoH). Primary care providers often do not have information about these determinants, which could be used to support equitable health system planning and care delivery. The a...
BACKGROUND: Existing machine learning studies for pneumonia mortality prediction are limited by small sample sizes, single-center designs, and lack of comprehensive external validation across diverse healthcare systems. No previous study has systemat...
BACKGROUND: Early mortality prediction in critically ill patients with cardiovascular disease remains challenging. This study aimed to develop and validate an ensemble machine learning (ML) model to predict 30-day mortality, comparing its performance...
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