AIMC Topic: Osteoporosis

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Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.

Journal of medical systems
Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were inves...

Risk Prediction of Low Bone Density in Elderly Patients with Supervised Machine Learning Algorithms.

Balkan medical journal
BACKGROUND: Low bone mineral density (BMD) is a common age-related condition that elevates the risk of fractures and mortality. Machine learning (ML) techniques offer a promising approach for early prediction using readily available clinical, biochem...

Development of Artificial Intelligence-Assisted Lumbar and Femoral BMD Estimation System Using Anteroposterior Lumbar X-Ray Images.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
The early detection and treatment of osteoporosis and prevention of fragility fractures are urgent societal issues. We developed an artificial intelligence-assisted diagnostic system that estimated not only lumbar bone mineral density but also femora...

The knowledge distillation-assisted multimodal model for osteoporosis screening.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Osteoporosis is characterized by reduced bone mass and deterioration of bone structure, yet screening rates prior to fractures remain low. Given its high prevalence and severe consequences, developing an effective osteoporos...

Investigating the potential risk of cadmium exposure on Osteoporosis: An integrated multi-omics approach.

Ecotoxicology and environmental safety
Osteoporosis (OP) is a chronic progressive bone disease, and its occurrence and development under cadmium exposure remain unclear. This study aims to explore the role of cadmium exposure in the pathogenesis of OP through a comprehensive analysis of m...

A vision transformer-convolutional neural network framework for decision-transparent dual-energy X-ray absorptiometry recommendations using chest low-dose CT.

International journal of medical informatics
OBJECTIVE: This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations f...

Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records.

International journal of medical informatics
BACKGROUND: Hip fractures are associated with reduced mobility, and higher morbidity, mortality, and healthcare costs. Approximately 90% of hip fractures in the elderly are associated with osteoporosis, making it particularly important to screen the ...

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.

American journal of human genetics
Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's reliance on strong parametric assumptions can introduce bias if these are violated. We describe a ma...