AIMC Topic: Radiography
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Deploying artificial intelligence in the detection of adult appendicular and pelvic fractures in the Singapore emergency department after hours: efficacy, cost savings and non-monetary benefits.
Singapore medical journal
Jul 19, 2024
INTRODUCTION: Radiology plays an integral role in fracture detection in the emergency department (ED). After hours, when there are fewer reporting radiologists, most radiographs are interpreted by ED physicians. A minority of these interpretations ma...
Estimating infant age from skull X-ray images using deep learning.
Scientific reports
Jul 18, 2024
This study constructed deep learning models using plain skull radiograph images to predict the accurate postnatal age of infants under 12 months. Utilizing the results of the trained deep learning models, it aimed to evaluate the feasibility of emplo...
Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks.
Medical physics
Jul 17, 2024
BACKGROUND: X-ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X-ray image quality control (QC) is essential. However, subjectively assessing image quality is ineffic...
Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients.
European journal of radiology
Jul 17, 2024
PURPOSE: To evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patients, for the detection of acute appendicular fractures in the pediatric population on conventional X-...
Enhanced enchondroma detection from x-ray images using deep learning: A step towards accurate and cost-effective diagnosis.
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Jul 15, 2024
This study investigates the automated detection of enchondromas, benign cartilage tumors, from x-ray images using deep learning techniques. Enchondromas pose diagnostic challenges due to their potential for malignant transformation and overlapping ra...
Deep learning application of vertebral compression fracture detection using mask R-CNN.
Scientific reports
Jul 15, 2024
Vertebral compression fractures (VCFs) of the thoracolumbar spine are commonly caused by osteoporosis or result from traumatic events. Early diagnosis of vertebral compression fractures can prevent further damage to patients. When assessing these fra...
The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis.
European radiology
Jul 12, 2024
OBJECTIVES: Knee osteoarthritis (KOA), a prevalent degenerative joint disease, is primarily diagnosed through X-ray imaging. The Kellgren-Lawrence grading system (K-L) is the gold standard for evaluating KOA severity through X-ray analysis. However, ...
AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review.
European journal of trauma and emergency surgery : official publication of the European Trauma Society
Jul 9, 2024
PURPOSE: Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accura...
Assessment of Pelvic Tilt in Anteroposterior Radiographs by Area Ratio Based on Deep Learning.
Spine
Jul 8, 2024
STUDY DESIGN: Diagnostics.
Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.
Arthritis & rheumatology (Hoboken, N.J.)
Jul 8, 2024
OBJECTIVE: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic...
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