Orthopedics

AI-powered biomechanical modeling for ACL-reconstructed knees: predicting knee joint contact forces via computer vision and deep learning.

BACKGROUND: Patients undergoing anterior cruciate ligament reconstruction (ACLR) are at high risk of osteoarthritis or secondary injuries, with abnormal knee contact forces (KCFs) identified as a key factor in joint degeneration. Traditional KCF assessment relies on expensive lab systems while advances in computer vision and AI now enable low-cost alternatives. However, currently available methods...

Diagnosing orthopaedic infection by identifying neutrophils in whole histology slide images with machine learning trained on publicly available datasets.

AIMS: This study examines the ability of YOLO (You Only Look Once) 11x, a widely used and state of the art object detection model, trained on publicly available datasets, to identify and count neutrophils in tissue samples taken at prosthetic joint revision surgery, with the objective of automating a laborious but necessary part of the diagnostic workup for periprosthetic joint infection. METHODS:...

The Development and External Testing of Deep Learning Model for Automated Classification of Intervertebral Disc Degeneration on CT Images.

The study aims to develop and externally test a deep learning (DL) model using CT images for automatic classification of intervertebral disc degenerat...

Robot-mediated haptic feedback outperforms vision in violin duo coordination.

Joint actions among humans rely on the integration of multiple sensory modalities, most notably auditory and visual cues, which support explicit commu...

AI-Based Personalized Therapy With Clinical Intelligence and Radiomics (SPOILS) for Patients With Low Back Pain: Prospective Observational Study.

BACKGROUND: Low back pain (LBP) is a leading cause of disability worldwide, affecting people of all ages while showing increasing prevalence among you...

An automated framework for quantitative alveolar bone loss using deep learning-based landmark detection.

OBJECTIVE: To develop and evaluate an automated framework for full-mouth quantification of radiographic alveolar bone loss (ABL) on panoramic radiogra...

Integrative lipidomics profiles and explainable machine learning for species-specific identification of meat and bone meal.

Accurate species identification of meat and bone meal (MBM) is a regulatory requirement in many jurisdictions to prevent intra-species recycling (cann...

ResNet based backbone integrated YOLO framework for bone fracture detection.

The usage of artificial intelligence and machine learning has significantly strengthened computer-aided medical diagnostics, and fine-tuning models an...

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 ai...

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...

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 (...