Orthopedics

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 accessible diagnostic tools. Traditional motion capture technology, while effective, is often cost-prohibitive and limited to laboratory settings. In response, we developed a smartphone-based approach utilizing spatiotemporal analysis of joint angular vel...

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 cartilaginous tumors (ACT). Histologically, tumor distinction of these entities is limited by sampling bias, while radiologically, similar lesion features render classification challenging. Therefore, the aim of this study is to investigate whether machine le...

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 (...

Teriparatide reduces spinal neuroinflammatory phenotypes in ovariectomized rats revealed by an artificial intelligence-driven glial cell morphometry of bright-field tissue sections.

Osteoporosis frequently presents with lower back pain, often accompanied by hypersensitivity, even in the absence of vertebral fractures, suggesting t...

Convolutional neural networks in paediatric fracture detection: pooled evidence from a systematic review and meta-analysis.

OBJECTIVE: The objective of this review was to systematically evaluate the diagnostic accuracy of artificial intelligence (AI) models for detecting pa...

The most important features in generalized additive models might be groups of features.

While analyzing the importance of features has become ubiquitous in interpretable machine learning, the joint signal from a group of related features ...

Concordance of ChatGPT, Gemini, Claude, and OpenEvidence with the 2024 AAOS guidelines on acute isolated meniscal pathology.

PURPOSE: To evaluate the reliability and clinical applicability of the three most commonly used large language models (LLMs) (ChatGPT, Gemini, and Cla...

Advances in Imaging-Based Fracture Risk Assessment for Unlocking Latent Skeletal Fragility.

From "high AUC" to evidentiary reliability: audit-ready deployment standards for deep-learning rib-fracture detection in postmortem CT.

Prediction of femoral shaft fracture healing and clinical decision support through integration of mechano-biological modeling and machine learning.

BACKGROUND AND OBJECTIVES: Current clinical practice in preoperative planning for femoral shaft fractures lacks tools capable of quantitatively predic...

Digital and artificial intelligence literacy in inflammatory rheumatic and degenerative joint diseases.

PURPOSE: The development of digital technologies and artificial intelligence (AI) in healthcare has highlighted the need for patients to improve digit...

Chronic phase of Chikungunya: understanding the impact of joint pain using data science and artificial intelligence.

BACKGROUND: Chikungunya is an arbovirus capable of affecting the musculoskeletal system of infected individuals. Furthermore, it has the potential to ...

Longitudinal Investigation of Postural Strategies Associated with Improvement in Gait Speed Following Adult Spinal Deformity Surgery using Machine Learning.

STUDY DESIGN: Retrospective longitudinal cohort study. OBJECTIVE: To identify postural strategies associated with improvement in gait speed following ...

3D human pose keypoints and corresponding joint angle calculation for vision-based WMSD risk assessments.

Vision-based pose estimation, which can utilise ordinary videos, has been applied to assess work-related musculoskeletal disorder (WMSD) risks as a le...