U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Abstract Machine learning (ML) and its subset, deep learning (DL), are branches of artificial
intelligence (AI) showing promising findings in the medical field, especially when applied to …

Background This study was performed to evaluate the clinical feasibility of a U-net for fully
automatic uterine segmentation on MRI by using images of major uterine disorders. Methods …

Study Design: Narrative review. Objectives: This review aims to present current applications
of machine learning (ML) in spine domain to clinicians. Methods: We conducted a …

Automatic segmentation of shoulder muscle MRI is challenging due to the high variation in
muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and …

While deep learning has catalyzed breakthroughs across numerous domains, its broader
adoption in clinical settings is inhibited by the costly and time-intensive nature of data …

Spinal Malalignment is a chronic disease that is widespread across the world. It causes
different diseases such as Stenosis, Scoliosis, Osteoporotic Fractures, Thoracolumbar …

Cerebrospinal fluid (CSF) hypovolemia is the core of spontaneous intracranial hypotension
(SIH). More than 1000 magnetic resonance myelography (MRM) images are required to …

Magnetic resonance imaging is currently the gold standard for the evaluation of spinal cord
injuries. Automatic analysis of these injuries is however challenging, as MRI resolutions vary …

Objective Aquaporin-4-antibody-seropositive (AQP4-IgG+) Neuromyelitis Optica Spectrum
Disorder (NMOSD) and Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disorder …