Orthopedics

Combination of artificial intelligence and chest computed tomography to assess bone mineral density.

OBJECTIVES: To evaluate the diagnostic accuracy of artificial intelligence-assisted opportunistic chest CT for osteoporosis/osteopenia screening in a Chinese population. METHODS: This retrospective study included 1306 adults (≥ 55 years) undergoing concurrent chest CT and DXA during physical examinations (Apr 2015 to May 2024). Exclusion criteria comprised vertebral fractures, spinal surgery, or c...

Precision medicine in orthopaedics: A review of current technologies and future directions.

PURPOSE: To synthesise the paradigm shift towards precision medicine in orthopaedics, where individual anatomical, biomechanical, molecular and kinematic characteristics are integrated into clinical decision-making. Unlike traditional approaches applying uniform protocols, this review outlines how precision orthopaedics aims to tailor surgical techniques, implant selection, component positioning a...

Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.

OBJECTIVES: To develop and do multicenter validation on an algorithm that screens for osteoporosis from abdominal CTs. METHODS: This is a diagnostic a...

Artificial intelligence to automatically identify reverse shoulder arthroplasty implant brands on postoperative radiographs including uncertainty quantification.

BACKGROUND: Identifying the brand of reverse shoulder arthroplasty (rTSA) implanted is key in the postoperative evaluation of patients, a process that...

AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.

PURPOSE: To develop and validate an AI method for automated quantification of whole-skeleton bone marrow (BM) metabolic activity using Carbon 11 (11C)...

Synovial exosomal type II collagen as a biomarker for osteoarthritis Progression: From molecular evaluation to AI-powered SERS-based diagnosis.

Osteoarthritis (OA) is a degenerative joint disease which lacks reliable biomarkers for monitoring disease progression. Current assessment methods rel...

Joint Associations of Intensity-Specific Physical Activity and Adiposity With Incident Microvascular Disease Among Prediabetes: A Prospective Cohort Study.

BACKGROUND: To examine the independent, stratified, and joint associations of physical activity (PA) and adiposity with microvascular diseases (MVDs) ...

Artificial Intelligence and Machine Learning for Osteoarthritis and Cartilage Assessment.

Osteoarthritis (OA) affects over 500 million people globally and poses diagnostic and management challenges due to its complex pathophysiology. Artifi...

Unraveling the shared genetic architecture of osteoarthritis and metabolic traits through multi-omics insights.

OBJECTIVE: Osteoarthritis (OA) often coexists with metabolic traits (MTs), causing significant disability. Our study aims to uncover the shared geneti...

Artificial intelligence, machine learning and omic data integration in osteoarthritis.

OBJECTIVE: Artificial intelligence (AI), particularly its subfield of machine learning (ML), offer promising tools for integrating and interpreting hi...

Small language models: The big play for agentic artificial intelligence in orthopaedics.

While the integration of artificial intelligence in orthopedics is accelerating, the focus has largely been on powerful but resource-intensive Large L...

Swin-EffuseNet: A dual-stream attention-based model combining Swin transformer V2 and EfficientNet-BO for bone fracture classification.

CONTEXT: Bone fractures are among the most common musculoskeletal injuries and require timely, accurate diagnosis to ensure effective treatment and pr...

Free-breathing dynamic MRI reconstruction via joint time-dependent coil sensitivity estimation using implicit neural representation.

Dynamic magnetic resonance imaging (MRI) often encounters a trade-off between spatial and temporal resolutions due to slow data acquisition, particula...

Beyond self-reports after anterior cruciate ligament injury - machine learning methods for classifying and identifying movement patterns related to fear of re-injury.

Anterior cruciate ligament (ACL) tears are prevalent career-ending sports injuries. A barrier to successful return to activity is fear of re-injury. E...

A deep learning approach to predicting hospitalized patients' SEIRD states using multimodal spatiotemporal data.

BACKGROUND AND OBJECTIVE: The increasing prevalence of hospital-acquired infections (HAIs) due to antimicrobial resistance presents a formidable chall...

Tridimensional analysis of the normal upper airways in non-dentofacial deformity patients.

OBJECTIVES: To establish normative three-dimensional airway measurements in patients without dentofacial deformities (DDFs) or obstructive sleep apnea...

Real-world diagnostic performance of knee MRI protocols accelerated using simultaneous multi-slice acquisition and deep learning reconstruction.

OBJECTIVE: To assess whether accelerated knee MRI protocols using simultaneous multi-slice (SMS) and deep learning reconstruction (DLR) are non-inferi...

Artificial intelligence in early onset scoliosis: a scoping review.

PURPOSE: Early onset scoliosis comprises spinal deformities in children younger than 10, creating challenges in diagnosis, risk assessment, and manage...

Performance of machine learning algorithms in predicting the need for surgical fixation in pediatric craniomaxillofacial trauma.

Machine learning offers a novel approach to improve surgical triage in pediatric craniomaxillofacial trauma, where decision-making often relies on cli...

Comprehensive deep learning-assisted multi-condition analysis of knee MRI studies improves resident radiologist performance.

OBJECTIVES: Developing a deep-learning model for automated multi-tissue, multi-condition knee MRI analysis and assessing its clinical potential. MATER...