Orthopedics

Which matters more for preventing knee replacements: reducing overactivity or combating inactivity?

BACKGROUND: Daily physical activity (PA) has been gaining attention for the management and prevention of knee osteoarthritis (OA). AIMS: This study aimed to compare the effects of various hypothetical daily PA regimens on the incidence of knee replacement (KR) surgery using a targeted learning approach. METHODS: We analyzed data from the Osteoarthritis Initiative on adults in the US with symptomat...

The Emory Knee Radiograph (MRKR) Dataset.

The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of 503,261 knee radiographs from 83,011 patients, 40% of which are African American. This dataset provides imaging data in DICOM format along with detailed clinical information, including patient-reported pain scores, diagnostic codes, and procedural codes, which are not commonly available in similar datasets. ...

Machine learning-based prediction model for fall risk among individuals with arthritis in China: an analysis using the China Health and Retirement Longitudinal Study (CHARLS) database.

BACKGROUND: Falling has become a global public health problem. Individuals with arthritis have a higher risk of falling because of joint pain, poor mu...

Self-Powered Multisensor with Electromagnetic and Triboelectric Energy Harvesting for Wireless Monitoring in Every Knee Joint Motion.

Artificial Intelligence of Things (AIoT) enables convenient human health monitoring but consumes massive data and energy. Traditional power supplies a...

Artificial Intelligence Models for Predicting Triage in Emergency Departments: Seven-Month Retrospective Comparative Study of Natural Language Processing, Large Language Model, and Joint Embedding Predictive Architectures.

BACKGROUND: Triage errors in emergency departments (EDs), including undertriage and overtriage, pose significant risks to patient safety and resource ...

Molecular-level understanding of the aging bone and regeneration mechanisms using computational methods.

Bone degeneration diseases, such as osteoporosis, are skeletal disorders characterized by diminished bone mass and increased susceptibility to fractur...

Clinical implementation of AI for vertebral fracture detection in CT aligned with fracture liaison services: high prevalence of undiagnosed vertebral fractures.

UNLABELLED: We assessed feasibility and effectiveness of AI-based VF screening in CT, integrated with a local FLS. The system identified VFs in 14% of...

Utilization of machine learning to identify lower extremity biomechanical predictors of rupture in a validated cadaveric model of ACL injury.

Diagnostic Accuracy of Deep Learning for Automated Detection of Spinal Degenerative Disease on MRI: A Systematic Review and Meta-Analysis.

This study aims to estimate the diagnostic accuracy of deep learning (DL) models for automated detection/classification of spinal degenerative disease...

Knee osteoarthritis classification using Optimized Granger Causality Inspired Graph Neural Network with Accelerated Model-agnostic Explanations.

BACKGROUND: Knee osteoarthritis (KOA) is a common, degenerative joint condition where cartilage wears away, causing bone-on-bone friction, leading to ...

Predictive modeling of composite fracture toughness using machine learning.

OBJECTIVES: This study applied machine learning (ML) models to predict fracture toughness (K1c) of experimental ion-releasing resin-based composites a...

Benchmarking a deep learning model against healthcare practitioners for hip fracture detection in the emergency department.

INTRODUCTION: This study aimed to validate a deep learning (DL) model for automated hip fracture detection on pelvic X-rays in emergency departments (...

LRF-UNet: Low-Rank Factorized Convolution Deep-Learning Networks for Visceral Adipose and Muscle Tissue Segmentation in Abdominal Computed Tomography Image.

Quantitative analysis of skeletal muscle (SM) and visceral adipose tissue (VAT) cross-sectional volumes at the third lumbar vertebral level (L3) on ab...

Global patterns and predictors of initial treatment in early rheumatoid arthritis: insights from a multinational machine learning study.

BACKGROUND: Rheumatoid arthritis (RA) treatment guidelines recommend early initiation of disease-modifying antirheumatic drugs (DMARDs), but actual pr...

Usability of a deep learning platform for detecting radiographic bone loss and furcation involvement.

BACKGROUND: Assessing radiographic bone condition is important for periodontal diagnosis. The accuracy of radiographic interpretation depends highly o...

Accelerated Spine Magnetic Resonance Imaging: Techniques and Practical Implementation for Routine Clinical Imaging.

Spine magnetic resonance imaging is among the most frequently performed examinations in clinical radiology and places substantial demands on workflow ...

Integrating machine learning with SHAP to uncover multi-tissue molecular signatures in Osteoarthritis progression.

Osteoarthritis (OA) is a chronic joint disorder characterized by pain, reduced mobility, and structural degeneration. Despite its complex etiology and...

Investigation of the Medical Image Modalities to Detect the Abnormalities in the Spinal Cord Using Thresholding Techniques.

Accurately segmenting spinal structures from magnetic resonance imaging (MRI) is essential for diagnosing degenerative disc diseases. However, 1.5 T l...

IMMT downregulation promotes osteoarthritis development by inducing mitochondrial dysfunction.

OBJECTIVE: This study aimed to identify key mitochondria-related genes involved in the pathogenesis of osteoarthritis (OA). METHODS: Publicly availabl...

External validation of an AI-Based fracture detection tool for hip and pelvic radiographs in a multicenter retrospective cohort.

OBJECTIVE: Artificial intelligence tools show promise in fracture detection but may be impaired by hidden stratification. We aim to evaluate the diagn...