AIMC Topic: Absorptiometry, Photon

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Assessment of the risk of osteoporotic bone fracture in postmenopausal women using machine learning methods.

Scientific reports
The main objective of osteoporosis management is to prevent osteoporotic fractures. Using machine learning methods, new risk variables can be identified to enhance the ability to identify women with osteoporosis who are at an increased risk of bone f...

Face2Bone explainable AI model predicts osteoporosis risk from facial images in proof of concept study.

Scientific reports
OBJECTIVES: BMI and age are associated with the risk of osteoporosis (OP). The dynamic facial aging process involves changes in skin, muscle, fat, and facial bone structures, with facial skeletal aging affecting facial contours through volumetric red...

3D deep learning-based muscle volume quantification from thoracic CT as a surrogate for DXA-Derived appendicular muscle mass in older adults.

Aging clinical and experimental research
BACKGROUND: In order to identify patients with sarcopenia, the use of routine imaging could provide valuable support. One of the most common radiological examinations, especially in geriatric inpatient care, is CT thoracic imaging. Therefore, it woul...

Opportunistic screening of low bone mass using knowledge distillation-based deep learning in chest X-rays with external validations.

Archives of osteoporosis
UNLABELLED: Low bone mass (LBM), which can lead to osteoporosis, is often undetected and increases the risk of bone fractures. This study presents OsPenScreen, a deep learning model that can identify low bone mass early using standard chest X-rays (C...

Osteoporosis prediction from hand X-ray images using segmentation-for-classification and self-supervised learning.

Scientific reports
Osteoporosis is a prevalent metabolic bone disease that frequently remains undiagnosed due to limited access to bone mineral density (BMD) tests, such as Dual-energy X-ray absorptiometry (DXA). To address this issue, recent research explores alternat...

Association between fat-to-muscle ratio and secondary osteoporosis in rheumatoid arthritis: a cross-sectional study at a tertiary hospital in China.

BMJ open
OBJECTIVES: To investigate the correlation between fat-to-muscle ratio (FMR) or other body composition and secondary osteoporosis (OP) in patients with rheumatoid arthritis (RA) and to develop a predictive model using FMR and related clinical factors...

Construct prediction models for low muscle mass with metabolic syndrome using machine learning.

PloS one
BACKGROUND: Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posin...

AI-driven bone mineral density prediction from chest x-rays and its association with obstructive sleep apnea.

PloS one
With an increasing aging population, the prevalence of chronic comorbidities is on the rise. The potential relationship between obstructive sleep apnea (OSA) and osteoporosis has garnered significant attention. Most studies examining the association ...

Artificial intelligence-based diabetes risk prediction from longitudinal DXA bone measurements.

Scientific reports
Diabetes mellitus (DM) is a serious global health concern that poses a significant threat to human life. Beyond its direct impact, diabetes substantially increases the risk of developing severe complications such as hypertension, cardiovascular disea...

Gender difference in cross-sectional area and fat infiltration of thigh muscles in the elderly population on MRI: an AI-based analysis.

European radiology experimental
BACKGROUND: Aging alters musculoskeletal structure and function, affecting muscle mass, composition, and strength, increasing the risk of falls and loss of independence in older adults. This study assessed cross-sectional area (CSA) and fat infiltrat...