Latest AI and machine learning research in orthopedics for healthcare professionals.
BACKGROUND AND OBJECTIVE: The increasing prevalence of hospital-acquired infections (HAIs) due to antimicrobial resistance presents a formidable challenge to patient outcomes and resource allocation. Existing prediction models often operate at a population level, failing to provide the granular, individual patient-specific risk assessments crucial for targeted interventions. Our study addresses th...
OBJECTIVES: To establish normative three-dimensional airway measurements in patients without dentofacial deformities (DDFs) or obstructive sleep apnea (OSA), and to identify anatomical and epidemiological factors associated with airway volume. METHODS: This retrospective cross-sectional study analyzed 200 CT scans from patients aged 18-80 years, with no diagnosis of DDF, OSA, or craniofacial syndr...
OBJECTIVE: To assess whether accelerated knee MRI protocols using simultaneous multi-slice (SMS) and deep learning reconstruction (DLR) are non-inferi...
PURPOSE: Early onset scoliosis comprises spinal deformities in children younger than 10, creating challenges in diagnosis, risk assessment, and manage...
Machine learning offers a novel approach to improve surgical triage in pediatric craniomaxillofacial trauma, where decision-making often relies on cli...
OBJECTIVES: Developing a deep-learning model for automated multi-tissue, multi-condition knee MRI analysis and assessing its clinical potential. MATER...
BACKGROUND: International protocols for age estimation in subadults recommend combining different evidence according to tooth and bone maturity by rad...
Transcranial ultrasound imaging plays an important role in the diagnosis of brain diseases and the monitoring of brain function. However, the quality ...
BACKGROUND CONTEXT: As the population ages, rates of lumbar spine disease have risen, and lumbar fusion surgeries have become more prevalent. There ha...
RATIONALE & OBJECTIVE: Low muscle mass is a risk factor for chronic kidney disease. In this study, we examined the relationship between muscle mass an...
MR imaging of the triangular fibrocartilage complex (TFCC) is technically demanding and highly dependent on the use of optimal tools, including the sc...
CONTEXT: Experimental evidence supporting the existence of the viscerosomatic reflex highlights an involvement of multiple vertebral levels when renal...
BACKGROUND CONTEXT: Radiomics, a technique employing machine learning (ML) to extract quantitative features from processed radiographic images, holds ...
PURPOSE: To evaluate the image quality of pediatric portable chest radiographs processed using a deep learning-based noise reduction (NR) algorithm im...
OBJECTIVES: To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including de...
INTRODUCTION: The digital age offers vast health information, boosting patient health literacy but also fostering challenges like misinformation. Larg...
BACKGROUND: The automated segmentation of maxillary and mandibular bones in cone-beam computed tomography (CBCT) using artificial intelligence (AI) is...
OBJECTIVE: This study investigates the use of neural networks to predict potential osteoporotic metabolic conditions using the Panoramic Mandibular In...
Leukemia remains a prevalent hematologic malignancy, and its morphological heterogeneity presents challenges for reliable identification under optical...
PURPOSE: To identify baseline clinical signs and symptoms associated with response to intense pulsed light (IPL) combined with meibomian gland express...