Orthopedics

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 abdominal computed tomography (CT) not only assists physicians in evaluating individual metabolic risk and nutritional status, but also aids clinicians in disease diagnosis, treatment planning, and prognostic evaluation. However, patient variability an...

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 prescribing decisions are influenced by multiple clinical and contextual factors. Machine learning (ML) offers a promising tool to uncover patterns in treatment selection and support personalized decision-making. OBJECTIVES: To identify the most import...

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...

Estimating proton density fat fraction using computed tomography attenuation in skeletal muscle and adipose tissue in a prospective clinical cohort.

BACKGROUND: Proton density fat fraction (PDFF) measured using magnetic resonance imaging (MRI) is considered a noninvasive reference measure of fat de...

Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets.

OBJECTIVE: Introduce a case study for Federated Learning (FL) in healthcare, addressing challenges posed by patient privacy and limited large-scale da...

Deviation From AI-Predicted Implant Sizes in Total Hip Arthroplasty Is Associated With Increased Complication Risk.

BACKGROUND: Patient characteristics may predict implant sizes in total hip arthroplasty (THA), but the clinical value and generalizability of such mod...

Machine learning applications for anterior cruciate ligament injury prediction and rehabilitation in sports: A scoping review with evidence synthesis.

PURPOSE: To map and synthesise current evidence on machine learning (ML) applications for anterior cruciate ligament (ACL) injury risk estimation, reh...

Multimodal Deep Learning-Based Screening of Degenerative Temporomandibular Joint Disease Using 2D Radiography: A Cost-Effective and Low-Radiation Approach.

OBJECTIVES: This study aimed to develop and preliminarily validate a multimodal deep learning model based on two-dimensional maxillofacial imaging for...

The application of machine learning-assisted serum SERS technology in the early screening and prognosis evaluation of osteoporosis.

Early detection, early intervention, early treatment, and timely prognostic monitoring of osteoporosis are crucial for improving patients' quality of ...

Evaluating Generative Artificial Intelligence Models' Responses to Questions About Scaphoid Fracture and Scaphoid Nonunion.

PURPOSE: To evaluate and compare the responses of ChatGPT and Google Gemini to common patient questions about scaphoid fracture and scaphoid nonunion,...

Automated Segmentation of Augmented Bone After Transalveolar Sinus Floor Elevation Using Deep Learning.

This study aimed to evaluate the performance of deep learning models for segmenting the augmented bone following transalveolar sinus floor elevation (...

[Deep learning-based assessment of periodontal ligament area changes in maxillary central incisors under different orthodontic regimens using cone-beam CT images].

Objective: To investigate the differences in the changes of periodontal ligament area (PDLA) and related clinical indicators before and after maxillar...

Rejoining fragmented ancient bamboo slips with physics-driven deep learning.

Bamboo slips are a crucial medium for recording ancient civilizations in East Asia. However, many excavated bamboo slips have been fragmented into tho...

An Automated Framework for Mandibular Reconstruction: Evaluation and Clinical Application.

BACKGROUND: Mandibular reconstruction suffers from limitations in automation and objectivity. This study aimed to develop an automated framework to ad...

The feasibility of using deep learning technologies to preliminarily identify patients with advanced knee osteoarthritis via smartphone videos.