Orthopedics

Automated Annotation of Pain Chronicity in Patients With Back Pain by Using Electronic Health Records: Retrospective Study.

BACKGROUND: Chronic back pain is a severe health condition with underlying biopsychosocial factors that make diagnosis difficult, and pain chronicity has been shown to be an important variable for studying patient outcomes. Due to the absence of standardized criteria, pain chronicity needs to be manually annotated by clinicians in electronic health records (EHRs), which is not only time consuming ...

Identifying patients at risk for short-term adverse events after hip arthroscopy: a machine learning analysis of a national database.

PURPOSE: To develop and compare machine learning-based risk prediction models to identify patients at risk for short-term adverse outcomes (overnight admission, early complication, or readmission) after hip arthroscopy and to determine key predictive demographic and clinical factors. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) datab...

An algorithm for automated femoral leg length and offset calculations on pelvis radiographs.

INTRODUCTION: Manual measurement of leg length (LL) and offset can be tedious. This study developed an automated algorithm for measuring LL and offset...

Machine learning model integrating oral microbiota and clinical features for predicting osteoporosis and bone loss in high-altitude populations.

A Biologically Lubricating Allicin-Based Nanoplatform for Remodeling the Inflammation-Senescence Axis in the Treatment of Periodontitis.

Periodontitis is driven by a self-reinforcing cycle of persistent inflammation and cellular senescence, further exacerbated by pathogenic microbial co...

A machine learning-assisted Ag-TiO2 SERS platform for intraoperative osteomyelitis diagnosis.

Osteomyelitis is a progressive bone infection with high disability and recurrence rates. However, current diagnostic approaches, including bacterial c...

Longitudinal evaluation of intra-patient changes in computed tomography-based body composition measures.

OBJECTIVE: To report longitudinal intra-patient changes in CT-based body composition using fully automated AI tools in an adult patient sample. METHOD...

CT-Based Bone Habitat Radiomics for Predicting Risk of Vertebral Fracture in Older Adults: A Longitudinal Study.

Study design/settingRetrospective longitudinal study.PurposeOsteoporotic vertebral fractures (OVF) are common in middle-aged and elderly populations. ...

Artificial intelligence enabled prediction of hip fragility fracture risk using hip radiograph: an attractive approach yet more work needs to be done.

Artificial intelligence for automated detection of joint bleeding via ultrasound in hemophilia: advancing standardization.

BACKGROUND: Musculoskeletal ultrasound (US) is a noninvasive tool for joint assessment in persons with hemophilia. Early detection of joint bleeding u...

Fully automated, deep learning, cardiac CT-based multimodal network for cardiovascular risk stratification in high-risk perioperative patients.

AIMS: Major adverse cardiac events (MACE) significantly impact perioperative morbidity and mortality. We aimed to develop a fully automated multimodal...

Artificial Intelligence-Enabled Electrocardiographic Detection of Severe Aortic Stenosis Leading to Transcatheter Aortic Valve Replacement.

BACKGROUND: EchoNext is an artificial intelligence (artificial intelligence)-enabled electrocardiographic (ECG) model validated to detect unrecognized...

Variable Performance Across Radiography Vendors of a Commercial Artificial Intelligence System for Fracture Detection: A Multisite Evaluation.

A Review on Automatic Personal Identification Using Panoramic Radiographs and Computed Tomography.

Identifying completely unknown individuals is a major challenge in forensic and emergency medicine. Radiology offers a promising solution by using uni...

Thoracic muscle loss increases the use of mechanical ventilation in elderly patients with pulmonary embolism: constructing and validating a machine learning model on a two-center cohort.

OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict the need for mechanical ventilation (MV) in elderly patie...

Radiomics features as imaging biomarkers in alveolar and craniofacial bone healing: a scoping review.

Radiomics has emerged as a promising approach for quantifying bone regeneration by extracting high-dimensional features from routine imaging data. Thi...

Association between fundus sex index and hypertension, diabetes mellitus, and smoking in the Kumejima study.

Fundus parameters can be used to quantify masculinity or femininity as a fundus sex index (FSI) ranging from 0 to 1. The purpose of this study was to ...

Predicting gait kinematics in youth with cerebral palsy using clinically informed machine learning algorithms.

BACKGROUND: Gait deviations are common in youth with Cerebral Palsy (CP), with the change in gait pattern during growth/development being influenced b...

Integration of multi-omics and machine learning to reveal the anti-osteoporosis effects of Hederagenin by inhibiting lipid peroxidation via the GPX4/p-STAT1/PTGS2 axis.

Osteoporosis (OP) is a multifactorial disease, leading to abnormal bone remodeling, requiring targeted therapeutic interventions. This study investiga...

Development and validation of a deep learning markerless system for lower-limb kinematics in hip and knee osteoarthritis population.

With the advancement of deep learning technology, markerless systems have emerged as a cost-effective and user-friendly alternative to marker-based sy...