Orthopedics

Artificial Intelligence for Opportunistic Screening for Osteoporosis and Spine Fractures Using Computed Tomography: A Systematic Review and Meta-Analysis.

OBJECTIVE: Osteoporosis is a major global health burden and often remains undiagnosed until fragility fractures occur. Dual-energy x-ray absorptiometry (DXA) is underutilized in routine practice. Computed tomography (CT) scans obtained for other indications offer an opportunity for opportunistic bone quality assessment without additional radiation exposure. This systematic review and meta-analysis...

Machine Learning for Evaluating the Heterogeneous Effects of Intensive In-Hospital Rehabilitation During the Postacute Phase After Hip Fracture Surgery on Activities of Daily Living.

BACKGROUND: It remains unclear which patient subgroups benefit most from intensive rehabilitation therapy. OBJECTIVES: This study aimed to examine the heterogeneity in the effects of intensive postacute rehabilitation after hip fracture surgery on activities of daily living and to identify its potential sources. RESEARCH DESIGN: A retrospective observational study. SUBJECTS: Patients aged 50 years...

The future of DXA: How AI is transforming bone health diagnostics.

Dual-energy X-ray absorptiometry (DXA) remains the clinical gold standard for assessing bone mineral density (BMD), guiding diagnosis and therapeutic ...

Deep Learning for Opportunistic Vertebral Fracture Detection on Routine Thoraco-abdominal Computed Tomography: A Systematic Review and Hierarchical Summary Receiver Operating Characteristic Meta-analysis of Patient-level Diagnostic Test Accuracy.

RATIONALE AND OBJECTIVES: Vertebral fractures (VFs) are common, clinically important, and often missed on routine chest or abdominal computed tomograp...

Deep learning-enhanced multiscale permeability prediction in fractured-vuggy carbonate reservoirs.

Predicting equivalent permeability in fractured-vuggy carbonate reservoirs is critical for optimizing hydrocarbon recovery and aquifer management yet ...

Metaheuristic hyperparameter optimization of deep neural networks for demographic-aware autism spectrum disorder classification.

Autism Spectrum Disorder (ASD) classification from neuroimaging data poses significant challenges due to high data heterogeneity and the complexity of...

Deep learning-based effusion-synovitis volume measured on MRI is associated with osteoarthritis progression: A longitudinal analysis of data from the osteoarthritis initiative.

OBJECTIVE: To investigate whether the 48-month change in effusion-synovitis volume (ΔESV) is associated with concurrent knee osteoarthritis progressio...

Associations between body composition and radiotherapy-related side-effects and health-related quality of life in patients with prostate or lung cancer: sub-analysis of the REQUITE trial.

BACKGROUND: Body composition is emerging as a prognostic biomarker in cancer and may be associated with treatment tolerance, side-effects, and health-...

Artificial intelligence-based quantitative computed tomography assessment of shoulder muscle and its association with outcomes after reverse total shoulder arthroplasty.

BACKGROUND: Muscle atrophy and fatty infiltration of the shoulder muscles are known to influence clinical outcomes after reverse total shoulder arthro...

Myokines in exercise‑mediated bone homeostasis: Molecular signaling mechanisms and therapeutic implications for bone disorders (Review).

Skeletal muscle functions as an endocrine organ, secreting myokines that mediate interorgan communication with bone. Exercise‑induced myokines regulat...

Artificial intelligence-guided clavicle fracture detection on plain radiographs: A retrospective diagnostic accuracy study.

Extensive research exists on artificial intelligence (AI)-assisted fracture detection in various anatomical regions, but clavicle fractures remain com...

Sensitivity gains under theoretical AI assistance in paediatric elbow fracture detection: a methodological concern and clinical implications.

This letter discusses methodological considerations arising from the study by Da Costa et al. on deep learning-assisted fracture detection in paediatr...

Consistent analgesic effect of intravenous dexamethasone on rebound pain after brachial plexus block: a causal machine learning approach.

BACKGROUND: Rebound pain, a sudden increase in pain intensity after resolution of regional anesthesia, remains a major challenge in postoperative pati...

A Physics-Informed Deep Learning Framework for Estimating Muscle Activation Patterns Following Achilles Tendon Repair.

Accurate soleus (SOL) activation assessment is essential for Achilles tendon rupture (ATR) recovery, yet direct measurement remains a clinical challen...

Assessing the clinical reliability of deep learning for vertebral fracture detection: a two-surgeon commentary.

Implementing a Commercial AI Fracture Detection Tool in Health Care Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability Framework: A Formative Evaluation Study.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance resource efficiency, improve patient treatment, and increase safety in health ca...

Advance Learning in Oncology - A CT Imaging Dataset of Lung Nodule Metastases from Bone and Soft Tissue Tumors.

Bone and soft tissue tumors (BSTTs) are prone to metastasize to the lungs to form pulmonary nodules, which are quite different from other metastatic n...

LumbarSR: A Paired Clinical CT and Photon-Counting Micro-CT Dataset for Human Lumbar Vertebrae.

Low back pain affects hundreds of millions of people and is associated with degenerative changes in the lumbar spine. Trabecular bone microarchitectur...

Annotated digital image correlation displacement fields from fatigue crack growth experiments on aluminium alloys.

We present a curated dataset of planar displacement fields from eight fatigue crack growth experiments obtained via full-field digital image correlati...

Risk prediction models based on machine learning for emergence delirium in elderly patients undergoing spine surgery: development and validation study.

BACKGROUND: Emergence delirium (ED) is a common complication in elderly patients undergoing surgery for degenerative spinal disease (DSD) and is assoc...