Latest AI and machine learning research in orthopedics for healthcare professionals.
To evaluate convolutional neural network (CNN) model training strategies that optimize the performance of calcaneus fracture detection on radiographs at different image resolutions. This retrospective study included foot radiographs from a single hospital between 2015 and 2022 for a total of 1,775 x-ray series (551 fractures; 1,224 without) and was split into training (70%), validation (15%), and ...
We propose a deep learning approach for detecting anterior cruciate ligament (ACL) tears from knee MRI using a dual-branch convolutional architecture. The model independently processes sagittal and coronal MRI sequences using EfficientNet-B2 backbones with spatial attention modules, followed by a late fusion classifier for binary prediction. MRI volumes are standardized to a fixed number of slices...
Current radiotherapy (RT) planning workflows rely on pre-treatment simulation CT (sCT), which can significantly delay treatment initiation, particular...
Regular physical activity preserves functional independence in older adults, yet care-home residents often miss out because personalized supervision i...
Scoliosis is the most common developmental spinal deformity, but its genetic underpinnings remain only partially understood. To enhance the identifica...
Musculoskeletal (MSK) modeling and ultrasound imaging (USI) are complementary techniques that, when combined with three-dimensional gait analysis (3DG...
Accurate disease prognosis is essential for patient care but is often hindered by the lack of long-term data. This study explores deep learning traini...
To evaluate how recent advances in deep learning can improve the construction of quantitative phenotypes for genome-wide association studies (GWAS), w...
Cognitive decline affects approximately 40% of Parkinson’s disease (PD) patients within 10 years of diagnosis, progressing to dementia in 80% of patie...
Osteoporosis is a major health concern in Vietnam due to a rise in aging rates. However, cost-effective early screening tools tailored to the Vietname...
This study explored the association between low-dose computed tomography (LDCT)-derived body composition and melanoma incidence risk. LDCT scans from ...
To investigate the performance of LLMs in radiology numerical tasks and perform a comprehensive error analysis. We defined six tasks: extracting 1-min...
Emergency Department (ED) overcrowding, often exacerbated by prolonged patient length of stay (LOS), is a global challenge. Patients presenting with s...
Coronary artery disease (CAD) is the leading cause of death worldwide, yet it is highly preventable. Early detection is critical, particularly because...
One of the main causes of permanent blindness in the globe, glaucoma frequently advances symptomlessly until it reaches an advanced stage. Recent deve...
Non-contrast CT head scans (NCCTH) are the most frequently requested cross-sectional imaging in the Emergency Department. While AI tools have been dev...
Accurate assessment of the severity of central canal stenosis (CCS) on lumbar spine MRI is critical for clinical decision-making. We evaluated deep le...
Immunotherapy has improved outcomes for advanced-stage melanoma, however, predictive biomarkers remain limited. We evaluated whether computed tomograp...
Large language models (LLMs) are increasingly used in healthcare, but standardized benchmarks fail to capture their validity and safety in real-world ...
The purpose of this study is to develop a deep neural network model for predicting soleus muscle activation patterns from time-series lower-limb joint...