OBJECTIVES: To develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs...
BACKGROUND: Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment.
Alzheimer's disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Current deep learning approaches, particularly those using traditional neural networks, face challenges such as handling high-dimensiona...
UNLABELLED: Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convoluti...
BACKGROUND: We aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T m...
OBJECTIVE: To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT).
Diagnostic and interventional radiology (Ankara, Turkey)
Sep 9, 2024
PURPOSE: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists.
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
Sep 9, 2024
BACKGROUND: A whole person approach to healthy aging can provide insight into social factors that may be critical. Digital technologies, such as mobile health (mHealth) applications, hold promise to provide novel insights for healthy aging and the ab...
BACKGROUND: To evaluate the clinical performance of two deep learning methods, one utilizing real clinical pairs and the other utilizing simulated datasets, in enhancing image quality for two-dimensional (2D) fast whole-body scintigraphy (WBS).
BACKGROUND: Prior studies characterizing worsening heart failure events (WHFE) have been limited in using structured healthcare data from hospitalizations, and with little exploration of sociodemographic variation. The current study examined the impa...