Orthopedics

Clinical Feasibility of Deep Learning Contrast Synthesis From MR Fingerprinting in Knee Osteoarthritis.

BACKGROUND: Magnetic Resonance Fingerprinting (MRF) enables rapid quantitative parameter mapping from which synthetic clinical contrast images can be derived using deep learning (DL). PURPOSE: This study evaluates the reliability and interchangeability of MRF-derived synthetic knee MRI relative to conventional MRI in patients with osteoarthritis. STUDY TYPE: Prospective single-center comparative s...

Effectiveness of Thoracic Paravertebral Block in Minimally Invasive Vertebroplasty: A Retrospective Cohort Study Focusing on Pain Trajectories and Sagittal Spinal Alignment.

PURPOSE: To evaluate the effectiveness of ultrasound-guided thoracic paravertebral block (TPVB) combined with general anesthesia (GA) versus GA alone in improving postoperative pain trajectories, reducing opioid consumption, and enhancing sagittal spinal alignment in patients undergoing minimally invasive vertebroplasty (MIVP) for osteoporotic vertebral compression fractures. DESIGN: Retrospective...

Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation.

Purpose To develop and validate deep learning models for detecting bone metastases on abdominal and thoracic CT scans, considering lesion visibility, ...

Hyperspectral imaging for orthopedic surgery: lightweight AI enables real-time bone and cartilage segmentation.

Long-term outcomes of lateral sinus floor elevation: A machine-learning analysis, systematic review, and meta-analysis of predictive factors.

OBJECTIVES: To quantify long-term (≥ 5 years) implant survival after lateral sinus floor elevation (LSFE) and to identify clinical predictors of long-...

Residual metric learning with class-specific consistency for multiclass classification.

Least squares regression (LSR) has been widely used in pattern recognition due to its concise form and ease of solution. However, inadequate explorati...

Comparison of Large Language Models with Rules-Based Natural Language Processing Algorithms for Extracting Data from Operative Notes.

BACKGROUND: We aimed to develop automated data extraction pipelines with large language models (LLMs) to extract registry data from total hip arthropl...

Establishment of a novel deep learning-based method for gait analysis after peripheral nerve injury in pigs.

BACKGROUND: Peripheral nerve injury with deficits has poor functional prognosis, making motor function assessment during nerve regeneration crucial. R...

EXPRESS: Identification of Acetylation-Related Molecular Signatures in Knee Osteoarthritis Patients with Significant Response to Warm-Needle Acupuncture Using Machine-Learning Approaches.

OBJECTIVE: This study aims to delineate remodeling effects of acetylation-related gene expression in knee osteoarthritis (KOA) patients markedly respo...

A vision transformer model for the detection of glaucoma from optic disc photographs.

This study uses a deep learning algorithm to analyze optic disc photographs (ODPs) and classify eyes as glaucomatous or healthy based on optic nerve a...

Predicting wear behavior of AZ31/TiC composites produced via ultrasonic vibration assisted friction stir processing using machine learning models.

This work aims to produce AZ31 Mg alloy reinforced with 15 vol.% TiC utilizing a unique Friction Stir mechanical vibration Processing (FSVP) technolog...

Smartphone-derived joint angular velocities in sit-to-stand motion provide a spatiotemporal marker for symptomatic knee osteoarthritis.

BACKGROUND: Knee osteoarthritis (OA) is a debilitating condition that compromises mobility and exacerbates knee pain, necessitating accurate and acces...

Machine learning vs. radiomics for discriminating atypical cartilaginous tumors from benign enchondromas on MRI.

BACKGROUND: Enchondromas (EC) present cartilaginous tumors that are difficult to differentiate from their intermediate counterpart, atypical cartilagi...

Segmentation and diagnosis of anterior cruciate ligament tear using deep learning and radiomics based on knee CT.

PURPOSE: Timely and accurate diagnosis of anterior cruciate ligament (ACL) tears has important clinical significance. In this study we tried to establ...

Multi-output LSTM-based prediction of postoperative delirium: integrating baseline and perioperative data for enhanced risk stratification in older spine surgery patients.

INTRODUCTION: Postoperative delirium (POD) adversely affects clinical outcomes among older adults undergoing spine surgery. However, existing predicti...

Predicting CT-based coronal plane knee phenotype parameters using imageless navigation and machine learning.

BACKGROUND: Coronal plane alignment of the knee (CPAK) categorizes knee phenotypes according to joint line obliquity (JLO) and the arithmetic hip‒knee...

Automatic framework for evaluating osteoarthritic cartilage severity: high-resolution cartilage thickness mapping and scoring.

OBJECTIVES: To develop and validate an automatic, scalable framework for assessing the femoro-tibial osteoarthritic cartilage severity using high-reso...

Optic Disc Drusen in Children: An Updated Clinical and Imaging Perspective.

BACKGROUND: The optic disc in a child with optic disc drusen (ODD) frequently mimics the optic disc in a child with papilledema, which may lead to unn...

Attention‑Guided Hybrid Deep Learning with Fibonacci and Annular Ring Handcrafted Features for Glaucoma Detection in Retinal Images.

Glaucoma, a major contributor to irreversible blindness, often progresses silently, making early detection vital. Manual interpretation of retinal fun...

The Synovial Fingerprint of Ankle Fractures: A Central Role for WNT Pathway Activation in Posttraumatic Ankle Osteoarthritis.

BACKGROUND: Osteoarthritis (OA) of the ankle is a disabling and understudied disease, most commonly emerging posttraumatically after ankle fractures (...