Orthopedics

Deep learning for automated 3D assessment of rotator cuff muscle atrophy and fat infiltration prior to total shoulder arthroplasty.

BACKGROUND: Rotator cuff muscle pathology affects outcomes following total shoulder arthroplasty, yet current assessment methods lack reliability in quantifying muscle atrophy and fat infiltration. We developed a deep learning-based model for automated segmentation of rotator cuff muscles on computed tomography (CT) and propose a T-score classification of volumetric muscle atrophy. We further char...

From algorithms to answers: a comparative analysis of popular search engines and large language models on clubfoot patient education.

This study evaluates Chat Generative Pre-Trained Transformer 4o's (ChatGPT-4o's) utility in clinical relevance and accuracy compared with Google for pediatric clubfoot treatment questions. Both were queried for the 15 most frequently asked questions related to pediatric clubfoot treatment, with Google as control. Questions were classified using the modified Rothwell criteria for online sources. Qu...

Predictive Value of Geriatric Nutritional Risk Index and Risk Analysis Index for Post-operative Outcomes in Spine Surgery Patients: A Comprehensive Analysis.

Study DesignRetrospective cohort study.ObjectivesFrailty and nutritional status are predictors of adverse spine surgery outcomes. This study evaluated...

Deep Learning-Based Detection of Papilledema on Retinal Photographs From Handheld Cameras: A Prospective Study.

BACKGROUND: Papilledema and other optic neuropathies are critical findings in neuro-ophthalmology that require timely and accurate diagnosis. This stu...

Deep Learning-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images.

Automated segmentation of skeletal muscle from computed tomography (CT) images is essential for large-scale quantitative body composition analysis. Ho...

Assessment of Multimodal Natural Language Processing in Ascertaining Perioperative Safety Indicators From Preoperative Notes in Spine Surgery.

INTRODUCTION: Standard spine surgery machine learning (ML) models often rely on structured clinical data, overlooking nuanced free text, such as preop...

Big Data in Neurosurgery: A Guideline on Data Structures, Machine Learning Models, and Ethical Considerations.

Artificial intelligence (AI) is reshaping neurosurgery, offering unprecedented opportunities to enhance diagnostics, personalize treatment, and predic...

Artificial Intelligence for Knee Osteoarthritis Care and Rehabilitation: A Systematic Review.

OBJECTIVES: To explore how artificial intelligence (AI) can improve the clinical and rehabilitation management of knee osteoarthritis (KOA), emphasizi...

A Deep Learning Tool for Hip Minimum Joint Space Width Calculation on Antero-posterior Pelvis Radiographs.

BACKGROUND: Minimum joint space width (mJSW) is a useful quantitative metric of osteoarthritis progression in the hip, particularly as a continuous va...

Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies.

INTRODUCTION: Since the 2000s, artificial intelligence (AI) publications in medicine have surged, particularly in orthopaedics and radiology. A key ar...

Are Artificial Intelligence Models Reliable for Clinical Application in Pediatric Fracture Detection on Radiographs? A Systematic Review and Meta-analysis.

BACKGROUND: Artificial intelligence (AI) applications for pediatric fracture diagnosis using radiographs have demonstrated growing potential in clinic...

Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models.

BACKGROUND AND OBJECTIVES: Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urge...

Development and validation of the APEX-HBD SCORe: a multivariable prediction model for postoperative complications in patients undergoing shoulder arthroplasty.

BACKGROUND: The incidence of total shoulder arthroplasty (TSA) has risen significantly, driven by expanded indications. This study aims to derive and ...

Predicting fracture toughness of human cortical bone from donors with and without type 2 diabetes using Raman spectroscopy and machine learning.

Type 2 diabetes mellitus (T2DM) is associated with increased skeletal fragility, yet standard clinical assessments often fail to detect diabetes-induc...

Medical image segmentation using dual-decoder mutual teaching with a mean teacher framework.

Accurate segmentation of medical images is essential for many clinical applications and is now typically achieved by training deep learning models on ...

Cluster Analysis of a Database of Surgically Managed Adult Spine Deformity (ASD) Patients: Determining the Minimal Clinically Important Difference for Each Cluster, Comparing Two Clustering Approaches.

STUDY DESIGN: Retrospective analysis of prospectively collected data. OBJECTIVE: To investigate whether two clustering approaches applied to the same ...

Deep Learning-Based Prediction for Bone Cement Leakage During Percutaneous Kyphoplasty Using Preoperative Computed Tomography: Model Development and Validation.

STUDY DESIGN: Retrospective study. OBJECTIVE: To develop a deep learning (DL) model to predict bone cement leakage (BCL) subtypes during percutaneous ...

A Scoping Review on AI-Supported Interventions for Non-Pharmacological Management of Chronic Rheumatic Diseases.

This review summarizes AI-supported non-pharmacological interventions for adults with chronic rheumatic diseases, detailing their components, purpose,...

Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.

Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a sign...

AI-driven robotic surgery in oncology: advancing precision, personalization, and patient outcomes.

Artificial intelligence (AI) integrated with robotic systems is transforming oncologic surgery by significantly improving precision, safety, and perso...