Orthopedics

Developing a metric for bone union in mandibular reconstruction using quantitative CT.

BACKGROUND: Objective quantification of bone union after mandibular reconstruction is important for evaluating reconstructive outcomes, yet current assessments are largely semi-quantitative. OBJECTIVE: To explore the feasibility of using opportunistic quantitative computed tomography (CT)-derived Hounsfield unit (HU) measurements, with and without machine learning, to characterize bone union after...

Integrating cervical vertebral maturation and spheno-occipital synchondrosis on CT: a non-linear framework for probabilistic age estimation.

The aim of this study was to compare the developmental timing and variability of cervical vertebral maturation (C2–C4) and spheno-occipital synchondrosis (SOS) ossification, and to evaluate these indicators using non-linear growth modelling and probabilistic age-threshold analysis to improve transparent quantification of uncertainty at legally relevant age thresholds (≥ 14, ≥ 16, and ≥ 18 years). ...

Enhanced Magnetic Resonance Imaging-Based Knee Cartilage Segmentation Using a Swin-UNet Conditional Generative Adversarial Network: Development and Validation Study.

BACKGROUND: Accurate segmentation of cartilage from magnetic resonance imaging (MRI) is crucial for the diagnosis and surgical planning of knee osteoa...

An integrated physics-guided machine learning approach for predicting asphalt concrete fracture parameters.

Prioritizing neglected food species in nutritional studies using expert-knowledge and explainable AI.

Food biodiversity is vital for human health and the development of sustainable food systems. However, research on neglected and underutilized species ...

Joint attention in autism: A narrative review of assessment techniques from behavioral observation to artificial intelligence.

Joint attention (JA), the shared focus between two individuals on an object or event, plays a pivotal role in social communication, cognitive developm...

Capturing the Multiscale Nature of Bone Behavior: Classical, Data-Driven and Hybrid Techniques.

The complexities of bone architecture, with its hierarchical organization and varying spatiotemporal scales, necessitate advanced modeling techniques ...

A Native Strategy for Integrating Deep-Learning Models for Segmentation into a Radiological Viewer.

The use of deep-learning (DL) models to support and automate medical imaging diagnostic procedures has become an ongoing focus of research and develop...

From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.

Bone tumors such as osteosarcoma and Ewing sarcoma remain among the most challenging cancers to diagnose and monitor because of their biological heter...

Integrating feature selection with unsupervised deep embedding for clustering single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of gene expression at the individual cell level, with clustering serving as a ...

Exosome-enabled bone defect repair: mechanistic foundations, bioengineered delivery, and artificial intelligence-driven translation.

Bone defects remain a substantial clinical burden. Exosomes have been extensively investigated as cell-free therapeutic candidates, exhibiting favorab...

Achilles tendon ruptures: still very much in the limelight, and still controversial. Not all research is as glittering as it may look.

Recent literature has renewed the never-ending interest in the management of Achilles tendon ruptures, but some articles have demonstrated inaccuracie...

Joint Sparse Optical Flow Estimation and Keypoint Detection via Dual-task Imperative Learning.

Contemporary deep learning approaches for optical flow estimation continue to face persistent challenges in model interpretability, generalization cap...

Artificial Intelligence for Predicting Clinical Outcomes in Interventional Pain Medicine for Spine Disorders: A Systematic Review.

OBJECTIVES: Artificial intelligence (AI) applications are being increasingly explored in pain medicine due to AI's ability to handle multidimensional ...

Artificial Intelligence-Based Exploration of the Relationship Between Running Motion Patterns and Achilles Tendon Stress in Inactive Adults Initiating a Running Program.

Inactive adults face a measurable risk of chronic Achilles tendon injury when starting to run, yet the mechanisms remain unclear. We leverage machine ...

Simulation-free spine palliative radiotherapy enabled by AI-adapted diagnostic CT.

BACKGROUND: Radiotherapy planning traditionally requires a dedicated simulation CT (sCT), which can introduce delays in initiating treatment. This is ...

Generalizability of deep learning-based dose conversion model in proton beam therapy.

BACKGROUND: In proton beam therapy (PBT), the analytical pencil beam (PB) algorithm involves dose uncertainties in inhomogeneous regions, making accur...

Predicting early discontinuation of adalimumab in patients with rheumatoid arthritis using machine learning: A specialty pharmacy-based approach.

BACKGROUND: Patients with rheumatoid arthritis (RA) prescribed adalimumab often discontinue treatment within 6 months because of a perceived lack of b...

Predictors of short-term, relapse-independent progression in multiple sclerosis: A machine learning approach based on clinical data and conventional MRI-derived features.

BACKGROUND: Progression independent of relapse activity (PIRA) contributes to long-term disability in multiple sclerosis (MS), even in early stages. H...

Probabilistic day-ahead forecasting of system-level renewable energy and electricity demand.

Increasing shares of wind and solar generation, together with rising electricity demand, introduce growing uncertainty into power system operations. A...