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Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population.

Frontiers in endocrinology
PURPOSE: Many high-risk osteopenia and osteoporosis patients remain undiagnosed. We proposed to construct a convolutional neural network model for screening primary osteopenia and osteoporosis based on the lumbar radiographs, and to compare the diagn...

Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network.

Dento maxillo facial radiology
OBJECTIVES: This study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using panoramic radiographs of women aged ≥ 50 years through three types of deep convolutional neural netwo...

Visual body composition assessment methods: A 4-compartment model comparison of smartphone-based artificial intelligence for body composition estimation in healthy adults.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Visual body composition (VBC) estimates produced from smartphone-based artificial intelligence represent a user-friendly and convenient way to automate body composition remotely and without the inherent geographical and monetary re...

Validity and reliability of a mobile digital imaging analysis trained by a four-compartment model.

Journal of human nutrition and dietetics : the official journal of the British Dietetic Association
BACKGROUND: Digital imaging analysis (DIA) estimates collected from mobile applications comprise a novel technique that can collect body composition estimates remotely without the inherent restrictions of other research-grade devices. However, the ac...

Comparison of computed tomography and dual-energy X-ray absorptiometry in the evaluation of body composition in patients with obesity.

Frontiers in endocrinology
OBJECTIVE: a) To evaluate the accuracy of the pre-existing equations (based on cm2 provided by CT images), to estimate in kilograms (Kg) the body composition (BC) in patients with obesity (PwO), by comparison with Dual-energy X-ray absorptiometry (DX...

Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network.

Journal of computer assisted tomography
OBJECTIVES: We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanner...

Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely available, low-cost test) can potentially solve this problem. In this study, we develop deep learni...

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. De...

Establish and validate the reliability of predictive models in bone mineral density by deep learning as examination tool for women.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models coul...

Automated vertebral bone mineral density measurement with phantomless internal calibration in chest LDCT scans using deep learning.

The British journal of radiology
OBJECTIVE: To develop and evaluate a fully automated method based on deep learning and phantomless internal calibration for bone mineral density (BMD) measurement and opportunistic low BMD (osteopenia and osteoporosis) screening using chest low-dose ...