AIMC Topic: Osteoporosis

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Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.

Scientific reports
Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a significantly increased risk of fragility fractures. To prevent such fractures in patients with cervical...

Letter to the Editor: Robustness of osteoporosis risk prediction models with enhanced statistical analyses.

Computers in biology and medicine
In response to Oka et al.'s letter, we conducted additional statistical analyses to validate the robustness of our osteoporosis risk prediction model using NHANES 2007-2014 data (n = 7924). We evaluated 10 key predictors through Spearman's rho, Kenda...

Letter to the Editor: Complementary statistical approaches for interpreting machine learning feature importance in osteoporosis risk.

Computers in biology and medicine
This paper comments on the valuable contribution by Carvalho and Gavaia regarding machine learning for osteoporosis risk prediction, particularly their use of a stacking ensemble model and feature importance analysis. While acknowledging the model's ...

A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes.

Head & face medicine
BACKGROUND: Orthopantomograms (OPGs) are essential diagnostic tools in dental and maxillofacial care, providing a panoramic view of the jaws, teeth, and surrounding bone structures. Detecting bone loss, which indicates periodontal disease and systemi...

Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.

BMC medical imaging
OBJECTIVE: This study aimed to develop and validate a predictive model to detect osteoporosis using radiomic features and machine learning (ML) approaches from lumbar spine computed tomography (CT) images during an abdominal CT examination.

Construction of a novel online calculator for prediction of osteoporosis risk in Chinese type 2 diabetes patients.

Experimental gerontology
BACKGROUND: Type 2 diabetes (T2D) has been established as an independent risk factor for osteoporosis, often resulting in a poor prognosis. Thus, it is crucial for clinicians to diagnose osteoporosis in diabetic patients. This study aimed to develop ...

Artificial intelligence for predicting the risk of bone fragility fractures in osteoporosis.

European radiology experimental
Osteoporosis is widespread with a high incidence rate, resulting in fragility fractures which are a major contributor to mortality among the elderly. Artificial intelligence (AI), in particular artificial neural networks, appears to be useful in mana...

Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations.

Current osteoporosis reports
PURPOSE OF REVIEW: Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underreprese...

Resting-State Functional MRI Reveals Altered Seed-Based Connectivity in Diabetic Osteoporosis Patients.

Clinical interventions in aging
BACKGROUND: Diabetic osteoporosis (DOP) can cause abnormal brain neural activity, but its mechanism is still unclear. This study aims to further explore the abnormal functional connectivity between different brain regions based on the team's previous...

Deep Learning-enhanced Opportunistic Osteoporosis Screening in Ultralow-Voltage (80 kV) Chest CT: A Preliminary Study.

Academic radiology
RATIONALE AND OBJECTIVES: To explore the feasibility of deep learning (DL)-enhanced, fully automated bone mineral density (BMD) measurement using the ultralow-voltage 80 kV chest CT scans performed for lung cancer screening.