AIMC Journal:
JBMR plus

Showing 1 to 9 of 9 articles

Radiomics and machine learning for characterizing CKD-associated cortical bone texture patterns in HR-pQCT tibia scans: a slice- and patient-level methodological framework.

JBMR plus Aug 4, 2026
Chronic kidney disease (CKD) is associated with alterations in cortical bone structure and composition that contribute to increased fracture risk but are incompletely captured by standard clinical imaging, including DXA. This study evaluated whether ...
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Development of an automated landmarking tool for the femur in dual-energy X-ray absorptiometry scans using contour-based image analysis.

JBMR plus Jun 19, 2026
Osteoporosis is a skeletal disease that significantly increases fracture risk and imposes a growing public health and economic burden. Notably, hip fractures are associated with high mortality, long-term disability, and loss of independence. When eva...
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Opportunities for deep learning techniques to advance the histological analysis of preclinical models of osteoarthritis beyond ordinal rank systems.

JBMR plus May 7, 2026
Tools for assessing disease progression are needed to identify and confirm new mechanisms driving osteoarthritis (OA) progression and guide therapeutic development. This review focuses on the advantages and feasibility of leveraging deep learning tec...
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Are machine learning models superior to logistic regression models to predict 30-day mortality post-hip fracture surgery?

JBMR plus Apr 28, 2026
Hip fractures represent a significant global health burden, with high mortality rates. Accurate prediction of 30-d postoperative mortality is instrumental to optimize patient care. This study aimed to validate previously developed logistic regression...
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A metabolomics-driven machine learning model for osteoporosis risk prediction.

JBMR plus Mar 16, 2026
Osteoporosis, marked by decreased bone mineral density (BMD), poses a major public health concern by increasing fracture risk, lowering quality of life, and raising healthcare costs in aging populations. Accurate risk prediction is essential for earl...
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Automated neural network femur segmentation performance in computed tomography images of older adults with obesity.

JBMR plus Jan 28, 2026
Femur segmentation is a precursor to image analysis pipelines that evaluate hip bone measures with subject-specific finite element models, but historically required time-intensive efforts of operators. Implementing deep learning techniques offers a p...
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Evaluating an Artificial Intelligence software for opportunistic low bone mineral density and osteoporosis screening: a validation study.

JBMR plus Dec 15, 2025
Despite the profound prevalence and fracture risk of osteoporosis, access to the gold standard DXA scans remains limited, especially in rural communities. Rho is an artificial intelligence software that can identify individuals at risk of low BMD and...
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Evaluating the cost-effectiveness of artificial intelligence-enhanced osteoporosis screening in men and women using routine chest radiographs in South Korea.

JBMR plus Dec 12, 2025
South Korea, now a "super-aged" society, faces a rising burden of fragility fractures, yet underdiagnosis remains a major barrier, with limited DXA access restricting early detection. Artificial intelligence (AI) applied to routine chest radiographs ...
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Uncemented hip arthroplasty and denosumab: increased postoperative dipeptide concentrations and identification of potential new bone turnover biomarkers.

JBMR plus May 19, 2025
Denosumab is a potent antagonist of RANKL and is widely used to treat severe postmenopausal osteoporosis. Using high-resolution mass spectrometry (HRMS), we aimed to identify molecular mediators associated with the rapid reactivation of osteoclasts f...
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