The utilization of artificial intelligence (AI) is expanding significantly within medical research and, to some extent, in clinical practice. Deep learning (DL) applications, which use large convolutional neural networks (CNN), hold considerable potential, especially in optimizing radiological evaluations. However, training DL algorithms to clinical standards requires extensive datasets, and their processing is labor-intensive.
View Article and Find Full Text PDFBackground: The reliability and diagnostic accuracy of commonly used diagnostic imaging modalities in the classification of lumbosacral transitional vertebrae (LSTV) are poorly known, and comparative studies are scarce.
Purpose: To compare the diagnostic performance of conventional radiography (CR), computed tomography (CT), and magnetic resonance imaging (MRI) in classifying LSTVs.
Material And Methods: In this retrospective cross-sectional study, a total of 852 patients undergoing lumbar imaging studies using all three modalities were initially assessed for the presence of LSTV using CT scans.
Int J Radiat Oncol Biol Phys
October 2024
Purpose: Changes in quantitative magnetic resonance imaging (qMRI) are frequently observed during chemotherapy or radiation therapy (RT). It is hypothesized that qMRI features are reflective of underlying tissue responses. It's unknown what underlying genomic characteristics underly qMRI changes.
View Article and Find Full Text PDFObjectives: To study the medial meniscus extrusion (MME) in subjects with and without medial meniscal tears on magnetic resonance imaging (MRI), supine ultrasound (US), and weight-bearing US.
Methods: Forty-seven cases (mean age 43.7 years) with medial meniscus tears and 53 healthy controls (mean age 36.