Orthopedics

Clinical impact of adopting an AI-driven opportunistic bone health screening software using routine X-ray.

OBJECTIVES: To evaluate the clinical impact of an artificial intelligence device, Rho, that opportunistically screens X-rays for low bone mineral density (BMD; DXA T-score < -1). MATERIALS AND METHODS: Over 13 months, Rho analyzed X-rays from patients ≥ 50 years at a large independent health facility. Radiologists could opt to include Rho-flagged findings in their X-ray reports. DXAs that occurred...

Artificial intelligence on opportunistic computed tomography for predicting vertebral fracture risk in women undergoing estrogen deprivation therapy for breast cancer.

PURPOSE: Fracture risk prediction in women exposed to hormone deprivation therapies (HDTs) for breast cancer is challenging, since bone mineral density may not be reliable in this context. This study aims to identify radiomic features (RFs) on opportunistic computed tomography (CT) images associated with vertebral fractures (VFs) in women under HDTs and to develop a radiomics-based model predictiv...

Radiomics-based nomogram using digital radiography for early diagnosis of knee osteoarthritis.

OBJECTIVES: Early diagnosis of knee osteoarthritis (KOA) remains challenging, particularly in distinguishing between Kellgren-Lawrence (KL) grades 1 a...

Surgical Robotic System for Precision Femoral Fracture Reduction.

Malalignment after femoral fracture repair remains common, with up to one-third of patients experiencing malrotations. Manual femoral fracture reducti...

A VLM guided network coupling degradation modeling for degradation aware infrared and visible image fusion.

Existing Infrared and Visible Image Fusion (IVIF) methods typically assume high-quality inputs. However, when handing degraded images, these methods h...

Characterization of acoustic emission parameters and identification of staged fracture propagation in solidified body-coal combination based on experimental and machine learning approaches.

Natural language generation in healthcare: A review of methods and applications.

OBJECTIVE: This study presents a systematic review of natural language generation (NLG) methods and applications in the medical domain, providing quan...

Predictive modelling of antibacterial efficacy in TiO2/ZnO/CS nanocomposites using artificial neural networks.

The contamination of water by drug-resistant pathogens underscores the urgent need for advanced antibacterial materials. In this study, we introduce a...

Rapid Design and Fabrication of Body Conformable Surfaces with Kirigami Cutting and Machine Learning.

By integrating the principles of kirigami cutting and data-driven modeling, this study aims to develop a personalized, rapid, and low-cost design and ...

Predicting total knee replacement in knee osteoarthritis using a machine learning-guided approach in patients of the Osteoarthritis Initiative (OAI).

OBJECTIVE: To develop a pragmatic model to predict total knee replacement (TKR) in knee osteoarthritis using non-imaging clinical, genetic and lifesty...

Automated deep learning pipeline for measuring lumbar thecal sac AP diameter on mid-sagittal MR images.

OBJECTIVE: To develop and validate an automated, disc-level deep learning pipeline for quantitative measurement of anteroposterior (AP) thecal sac dia...

Quantitative Assessment of the Extent of Retinal Vascularization in Retinopathy of Prematurity.

OBJECTIVE: To evaluate pixel-based measurements of the minimum nasal and temporal extent of retinal vascularization (NERV and TERV) from RetCam images...

Periodontal bone loss analysis via keypoint detection with heuristic post-processing.

OBJECTIVES: This study proposes a deep learning framework and an annotation methodology for the automatic detection of periodontal bone loss landmarks...

Reinjury risk after ACL reconstruction: a scoping review of artificial intelligence-based prediction models.

BACKGROUND: ACL reinjury after reconstruction remains a major challenge, affecting long-term function, return to sport and healthcare costs. Although ...

Machine Learning Algorithm to Predict Change in the Decision-Making for Thoracolumbar Fractures Without Neurological Deficit After MRI: A Multicenter Study.

Study DesignA multicenter study.ObjectiveTo develop a machine learning algorithm to predict when magnetic resonance imaging (MRI) may change the thora...

CBCT assisted diagnosis system for temporomandibular joint disc displacement based on deep learning.

BACKGROUND: The diagnosis of temporomandibular joint (TMJ) disc displacement relies on clinical symptoms and magnetic resonance imaging (MRI), which i...

Hip fracture detection on radiographs using an artificial intelligence-based support tool: a diagnostic accuracy study.

BackgroundAssessment of subtle hip fractures on radiographs can be difficult, especially among less experienced emergency physicians, which may prolon...

Real-world evaluation of large language models in detecting drug-related problems: A clinical pharmacist-AI concordance study in hematology care.

IntroductionLarge language models (LLMs) offer potential as clinical decision support systems (CDSS) for detecting drug-related problems (DRPs), yet t...

SwinCup-DiscNet: A fusion transformer framework for glaucoma diagnosis using optic disc and cup features.

Glaucoma remains a critical cause of permanent global visual disability, and is produced by advancing destruction of the visual nerve head (ONH). Earl...

Horizontal nystagmus identification with joint SAM segmentation and time series classification.

PURPOSE: Nystagmus is an involuntary jerky eye movement. It is an oculomotor sign of asymmetry in the vestibular pathway. With the rapid advancement o...