Orthopedics

Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES.

The combined effect of traffic-related pollutant mixtures on osteoporosis (OP) remains unclear. This study aimed to evaluate such associations using machine learning and mixture modeling approaches. A cross-sectional analysis was conducted among 3053 participants from the National Health and Nutrition Examination Survey 2015 to 2018. Eighteen exposures were assessed, including heavy metals, polycy...

From Technological Innovation to Clinical Translation: Progress and Challenges in 3D Bioprinting for the Development of Breast Cancer Bone Metastasis Models.

The construction of 3D in vitro tumor models centered on bioprinting technology represents one of the most anticipated interdisciplinary innovations and medical applications in recent years. Breakthrough research achievements have been made in the construction of 3D-printed models for various tumors, with a small subset of these technologies advancing to the clinical research stage. However, due t...

Integration of network pharmacology, deep learning, and molecular biology reveals the efficacy of Citrus aurantium L. var. amara Engl. blossom extract in ameliorating diabetic osteoporosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic osteoporosis (DOP) is a type of metabolic bone disease. Blossom of Citrus aurantium L. var. amara Engl. (CAVA...

Advances in Clinical Imaging and AI Integration for Dry Eye Diagnosis.

Dry eye disease (DED) is a prevalent multifactorial chronic disorder of the ocular surface, characterized by a loss of tear film homeostasis and accom...

Network Pharmacological Analysis of Lentinan in Regulating Intervertebral Disc Degeneration: Combined with Machine Learning-Based Screening and Molecular Dynamics Validation.

Lentinan (LNT) is a polysaccharide with antioxidant and anti-inflammatory properties; however, its link to intervertebral disc degeneration (IVDD) rem...

Compliance with the CONSORT AI Extension in Orthopaedic Randomized Controlled Trials.

BACKGROUND: The utilization of artificial intelligence (AI), such as machine learning, in health care has risen exponentially, underscoring the necess...

Utilization of artificial intelligence-based writing assistance in contemporary spine literature.

OBJECTIVE: With the advent of artificial intelligence (AI), scientific research and writing has benefitted from large language models to generate hypo...

Interlayer-aware postoperative facial appearance prediction in orthognathic surgery with bio-geometric guidance.

OBJECTIVE: Accurate prediction of postoperative facial appearance is essential for orthognathic surgical planning, yet remains challenging due to the ...

A hybrid AI vision model for single-camera tracking of rehabilitation exercises: a proof of concept in clinical settings.

Accurate measurement of range of motion (ROM) during physical rehabilitation is traditionally achieved using goniometers or multi-camera, marker-based...

O²RDL-net for joint risk classification and delay forecasting in logistics systems using interaction amplified deep.

Transportation and logistics systems face frequent delays and operational risks due to congestion, human factors, and rapidly changing conditions. Pre...

Optimizing preoperative planning for total hip arthroplasty using random forest models to predict stem size and compatibility.

BACKGROUND: Preoperative planning is essential for optimal outcomes post-total hip arthroplasty (THA). Appropriate stem size and compatibility with fe...

Markerless motion capture methodologies in biomechanics: From 2D images to 3D joint angles, a narrative review.

BACKGROUND: Video-based markerless motion capture is rapidly emerging as a valuable tool in biomechanics research, particularly within sports science,...

Machine learning-driven multi-objective optimization of Gyroid bioglass scaffolds for site-specific biomechanical adaptation.

The clinical translation of scaffold-driven bone regeneration is hindered by an inherent topological paradox between mechanical integrity and mass tra...

Radiation dose has no significant impact on CT-based bone mineral density measurements in a large-animal model.

Bone mineral density (BMD) is a biomarker for frailty, and CT-derived radiodensity can be extracted fully automatically as a surrogate. Because these ...

Joint trajectories of sleep duration and depressive symptoms and risk of incident multimorbidity: a longitudinal analysis with machine learning prediction.

BACKGROUND: Sleep disturbances and depressive symptoms frequently co-occur in older adults. Both conditions follow distinct, time-varying trajectories...

From Three to Eight Clusters: A Machine Learning-Based Classification of Operated Adult Spinal Deformity Patients.

BACKGROUND: Adult spinal deformity (ASD) is a heterogeneous condition encompassing diverse etiologies, clinical presentations, and surgical challenges...

Tree-based and sparse logistic models for predicting one-month postoperative performance status after surgery for spinal metastases.

PURPOSE: We aimed to develop and internally validate prediction models for one-month postoperative performance status (PS) after surgery for spinal me...

Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction.

OBJECTIVE: Deep learning-based noise reduction enhances image quality, overcoming the tradeoff among acquisition time, spatial resolution, and signal-...

Impact of Unfractionated Heparin Use on Maternal Bone Mineral Density During Pregnancy: A Retrospective Study Using AI-Assisted Radiographic Analysis.

PURPOSE: This study aimed to evaluate the impact of unfractionated heparin (UFH) use during pregnancy on maternal bone mineral density (BMD) by assess...

Armadillo-inspired active morphing skeletons for soft machines.

Armadillos can rapidly reconfigure their body into a rigid, enclosed sphere in active response to external threats, combining adaptive shape change wi...