AIMC Topic: Absorptiometry, Photon
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Can DXA image-based deep learning model predict the anisotropic elastic behavior of trabecular bone?
Journal of the mechanical behavior of biomedical materials
Sep 15, 2021
3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the rec...
Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis.
Clinical imaging
Aug 26, 2021
BACKGROUND: Osteoporosis is an underdiagnosed and undertreated disease worldwide. Recent studies have highlighted the use of simple vertebral trabecular attenuation values for opportunistic osteoporosis screening. Meanwhile, machine learning has been...
Bone strain index as a predictor of further vertebral fracture in osteoporotic women: An artificial intelligence-based analysis.
PloS one
Feb 8, 2021
BACKGROUND: Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disability, mainly when several subsequent fractures occur. A fragility fracture predictive model, Artifici...
Artificial intelligence for the detection of vertebral fractures on plain spinal radiography.
Scientific reports
Nov 18, 2020
Vertebral fractures (VFs) cause serious problems, such as substantial functional loss and a high mortality rate, and a delayed diagnosis may further worsen the prognosis. Plain thoracolumbar radiography (PTLR) is an essential method for the evaluatio...
Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study.
Bone
Jul 28, 2020
Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lum...
DXA measured body composition predicts blood pressure using machine learning methods.
Journal of clinical hypertension (Greenwich, Conn.)
Jun 4, 2020
Automatization and improvement of μCT analysis for murine lung disease models using a deep learning approach.
Respiratory research
May 24, 2020
BACKGROUND: One of the main diagnostic tools for lung diseases in humans is computed tomography (CT). A miniaturized version, micro-CT (μCT) is utilized to examine small rodents including mice. However, fully automated threshold-based segmentation an...
Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network.
European radiology
Feb 14, 2020
OBJECTIVES: To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images.
The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population.
Skeletal radiology
Nov 23, 2019
OBJECTIVE: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray feat...
Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry.
Radiology
Sep 17, 2019
Background Detection of vertebral fractures (VFs) aids in management of osteoporosis and targeting of fracture prevention therapies. Purpose To determine whether convolutional neural networks (CNNs) can be trained to identify VFs at VF assessment (VF...
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