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Custom-Made 3D-Printed Augments and Cages: An Effective Solution for Managing Severe Acetabular Bone Loss.
J Orthop Case Rep
January 2025
Department of Orthopaedic, Sunshine Bone and Joint Insitute, KIMS-Sunshine Hospitals, Hyderabad, Telangana, India.
Introduction: Total hip arthroplasty (THA) is recognized as one of the most effective surgical procedures for the treatment of end-stage hip arthritis. However, the increasing number of primary THA cases has led to a corresponding rise in the frequency of revision surgeries, which are often more complex and challenging due to severe acetabular bone loss. In such cases, managing Paprosky type 3A and 3B defects requires precise implant design and advanced surgical techniques.
View Article and Find Full Text PDFNormative vertebral deformity measurements in a clinically relevant population using magnetic resonance imaging.
World J Radiol
December 2024
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States.
Background: Osteoporosis is the leading cause of vertebral fractures. Dual-energy X-ray absorptiometry (DXA) and radiographs are traditionally used to detect osteoporosis and vertebral fractures/deformities. Magnetic resonance imaging (MRI) can be utilized to detect the relative severity of vertebral deformities using three-dimensional information not available in traditional DXA and lateral two-dimensional radiography imaging techniques.
View Article and Find Full Text PDFArtificial Intelligence for Cervical Spine Fracture Detection: A Systematic Review of Diagnostic Performance and Clinical Potential.
Global Spine J
January 2025
Department of Orthopaedics, Phramongkutklao Hospital and College of Medicine, Bangkok, Thailand.
Study Design: Systematic review.
Objective: Artificial intelligence (AI) and deep learning (DL) models have recently emerged as tools to improve fracture detection, mainly through imaging modalities such as computed tomography (CT) and radiographs. This systematic review evaluates the diagnostic performance of AI and DL models in detecting cervical spine fractures and assesses their potential role in clinical practice.
Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.
Open Heart
January 2025
Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Background: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT).
Methods: The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1.
End-to-end deep-learning model for the detection of coronary artery stenosis on coronary CT images.
Open Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
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