Related references
Note: Only part of the references are listed.Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics
Robel K. Gebre et al.
ANNALS OF BIOMEDICAL ENGINEERING (2021)
Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report
Natalia Gorelik et al.
CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES (2021)
Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks
Yijie Fang et al.
EUROPEAN RADIOLOGY (2021)
Computer-Aided Detection of Incidental Lumbar Spine Fractures from Routine Dual-Energy X-Ray Absorptiometry (DEXA) Studies Using a Support Vector Machine (SVM) Classifier
Samir D. Mehta et al.
JOURNAL OF DIGITAL IMAGING (2020)
The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review
Kuofeng Hung et al.
DENTOMAXILLOFACIAL RADIOLOGY (2020)
Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift
Joseph E. Burns et al.
JOURNAL OF BONE AND MINERAL RESEARCH (2020)
Detection and localisation of hip fractures on anteroposterior radiographs with artificial intelligence: proof of concept
J. S. Yu et al.
CLINICAL RADIOLOGY (2020)
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Ramprasaath R. Selvaraju et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2020)
The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population
Sangwoo Lee et al.
SKELETAL RADIOLOGY (2020)
A Deep Learning Approach to Antibiotic Discovery
Jonathan M. Stokes et al.
CELL (2020)
Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network
Koichiro Yasaka et al.
EUROPEAN RADIOLOGY (2020)
Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm
Chengyin Ye et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2020)
Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records
Yanshan Wang et al.
JOURNAL OF BIOMEDICAL INFORMATICS (2020)
Convolutional Neural Network for Second Metacarpal Radiographic Osteoporosis Screening
Nahom Tecle et al.
JOURNAL OF HAND SURGERY-AMERICAN VOLUME (2020)
Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network
Jian Liu et al.
JOURNAL OF MEDICAL SYSTEMS (2020)
An exemplar pyramid feature extraction based humerus fracture classification method
Sukru Demir et al.
MEDICAL HYPOTHESES (2020)
Improved protein structure prediction using potentials from deep learning
Andrew W. Senior et al.
NATURE (2020)
Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs
Ki-Sun Lee et al.
JOURNAL OF CLINICAL MEDICINE (2020)
Radiomics for classification of bone mineral loss: A machine learning study
S. Rastegar et al.
DIAGNOSTIC AND INTERVENTIONAL IMAGING (2020)
Osteoporotic hip fracture prediction from risk factors available in administrative claims data-A machine learning approach
Alexander Engels et al.
PLOS ONE (2020)
Precise proximal femur fracture classification for interactive training and surgical planning
Amelia Jimenez-Sanchez et al.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY (2020)
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
Myura Nagendran et al.
BMJ-BRITISH MEDICAL JOURNAL (2020)
Integrative blockwise sparse analysis for tissue characterization and classification
Keni Zheng et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)
Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men
Qing Wu et al.
CALCIFIED TISSUE INTERNATIONAL (2020)
Clinical Utility of Computer-Aided Diagnosis of Vertebral Fractures From Computed Tomography Images
Nithin Kolanu et al.
JOURNAL OF BONE AND MINERAL RESEARCH (2020)
Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification
Simukayi Mutasa et al.
JOURNAL OF DIGITAL IMAGING (2020)
Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey
Insha Majeed Wani et al.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2020)
Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning
E. Villamor et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)
Deep learning evaluation of pelvic radiographs for position, hardware presence, and fracture detection
Gene Kitamura
EUROPEAN JOURNAL OF RADIOLOGY (2020)
Research article The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs
Tsubasa Mawatari et al.
EUROPEAN JOURNAL OF RADIOLOGY (2020)
The Case for Algorithmic Stewardship for Artificial Intelligence and Machine Learning Technologies
Stephanie Eaneff et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2020)
Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation
Yasmeen Adar Almog et al.
JOURNAL OF MEDICAL INTERNET RESEARCH (2020)
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Beau Norgeot et al.
NATURE MEDICINE (2020)
FRAX: re-adjust or re-think
Yasser El Miedany
ARCHIVES OF OSTEOPOROSIS (2020)
Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women
Jae-Geum Shim et al.
ARCHIVES OF OSTEOPOROSIS (2020)
Artificial intelligence for the detection of vertebral fractures on plain spinal radiography
Kazuma Murata et al.
SCIENTIFIC REPORTS (2020)
A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study
Chi-Tung Cheng et al.
JMIR MEDICAL INFORMATICS (2020)
Prediction of trabecular bone architectural features by deep learning models using simulated DXA images
Pengwei Xiao et al.
BONE REPORTS (2020)
Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates
Norio Yamamoto et al.
BIOMOLECULES (2020)
Automatic Classifying of Patients With Non-Traumatic Fractures Based on Ultrasonic Guided Wave Spectrum Image Using a Dynamic Support Vector Machine
Jean-Gabriel Minonzio et al.
IEEE ACCESS (2020)
Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study
Bin Zhang et al.
BONE (2020)
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database
Stan Benjamens et al.
NPJ DIGITAL MEDICINE (2020)
Applied machine learning and artificial intelligence in rheumatology
Maria Hugle et al.
RHEUMATOLOGY ADVANCES IN PRACTICE (2020)
Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches
Salih Beyaz et al.
JOINT DISEASES AND RELATED SURGERY (2020)
A Novel Fracture Prediction Model Using Machine Learning in a Community-Based Cohort
Sung Hye Kong et al.
JBMR PLUS (2020)
From local explanations to global understanding with explainable AI for trees
Scott M. Lundberg et al.
NATURE MACHINE INTELLIGENCE (2020)
Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study
Jae-Seo Lee et al.
DENTOMAXILLOFACIAL RADIOLOGY (2019)
Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network
Takaaki Urakawa et al.
SKELETAL RADIOLOGY (2019)
A Novel Morphological Analysis of DXA-DICOM Images by Artificial Neural Networks for Estimating Bone Mineral Density in Health and Disease
Ehab I. Mohamed et al.
JOURNAL OF CLINICAL DENSITOMETRY (2019)
Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data
Uran Ferizi et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2019)
Discovery of potential biomarkers for osteoporosis using LC-MS/MS metabolomic methods
J. Wang et al.
OSTEOPOROSIS INTERNATIONAL (2019)
Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures
A. Valentinitsch et al.
OSTEOPOROSIS INTERNATIONAL (2019)
Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs
Chi-Tung Cheng et al.
EUROPEAN RADIOLOGY (2019)
Reporting of artificial intelligence prediction models
Gary S. Collins et al.
LANCET (2019)
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Diego Ardila et al.
NATURE MEDICINE (2019)
Machine Learning in Medicine
Alvin Rajkomar et al.
NEW ENGLAND JOURNAL OF MEDICINE (2019)
Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade
Arun Krishnaraj et al.
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY (2019)
Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions
Soterios Gyftopoulos et al.
AMERICAN JOURNAL OF ROENTGENOLOGY (2019)
Hip Fracture Discrimination Based on Statistical Multi-parametric Modeling (SMPM)
Julio Carballido-Gamio et al.
ANNALS OF BIOMEDICAL ENGINEERING (2019)
Artificial intelligence, osteoporosis and fragility fractures
Uran Ferizi et al.
CURRENT OPINION IN RHEUMATOLOGY (2019)
Artificial neural network optimizes self-examination of osteoporosis risk in women
Jia Meng et al.
JOURNAL OF INTERNATIONAL MEDICAL RESEARCH (2019)
Direct automated quantitative measurement of spine by cascade amplifier regression network with manifold regularization
Shumao Pang et al.
MEDICAL IMAGE ANALYSIS (2019)
Prediction of lumbar vertebral strength of elderly men based on quantitative computed tomography images using machine learning
M. Zhang et al.
OSTEOPOROSIS INTERNATIONAL (2019)
Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry
Sheldon Derkatch et al.
RADIOLOGY (2019)
Evaluation of hip fracture risk using a hyper-parametric model based on the Locally Linear Embedding technique
Enrique Nadal et al.
COMPTES RENDUS MECANIQUE (2019)
Artificial neural network to estimate micro-architectural properties of cortical bone using ultrasonic attenuation: A 2-D numerical study
Kaustav Mohanty et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2019)
Potential Liability for Physicians Using Artificial Intelligence
W. Nicholson Price et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2019)
How to Read Articles That Use Machine Learning Users' Guides to the Medical Literature
Yun Liu et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2019)
Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning
Urs J. Muehlematter et al.
EUROPEAN RADIOLOGY (2019)
Computer vs human: Deep learning versus perceptual training for the detection of neck of femur fractures
Matthew Adams et al.
JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY (2019)
Lifecycle Regulation of Artificial Intelligence- and Machine Learning-Based Software Devices in Medicine
Thomas J. Hwang et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2019)
Can low-frequency guided waves at the tibia paired with machine learning differentiate between healthy and osteopenic/osteoporotic subjects? A pilot study
Florian Vogl' et al.
ULTRASONICS (2019)
Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography
Kyoung Hyup Nam et al.
JOURNAL OF KOREAN NEUROSURGICAL SOCIETY (2019)
Can Classification and Regression Tree Analysis Help Identify Clinically Meaningful Risk Groups for Hip Fracture Prediction in Older American Men (The MrOS Cohort Study)?
Yi Su et al.
JBMR PLUS (2019)
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
Xiaoxuan Liu et al.
LANCET DIGITAL HEALTH (2019)
The global landscape of AI ethics guidelines
Anna Jobin et al.
NATURE MACHINE INTELLIGENCE (2019)
Deep learning predicts hip fracture using confounding patient and healthcare variables
Marcus A. Badgeley et al.
NPJ DIGITAL MEDICINE (2019)
Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population
A. S. Areeckal et al.
OSTEOPOROSIS INTERNATIONAL (2018)
Automated detection and classification of the proximal humerus fracture by using deep learning algorithm
Seok Won Chung et al.
ACTA ORTHOPAEDICA (2018)
Artificial intelligence on the identification of risk groups for osteoporosis, a general review
Agnaldo S. Cruz et al.
BIOMEDICAL ENGINEERING ONLINE (2018)
Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks
D. H. Kim et al.
CLINICAL RADIOLOGY (2018)
Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans
Naofumi Tomita et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2018)
Automated system for the detection of thoracolumbar fractures using a CNN architecture
U. Raghavendra et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2018)
Evaluation of low bone mass and prediction of fracture risk using metacarpal radiogrammetry method: a comparative study with DXA and X-ray phantom
Devaraj Ashok Kumar et al.
INTERNATIONAL JOURNAL OF RHEUMATIC DISEASES (2018)
Big Data and Machine Learning in Health Care
Andrew L. Beam et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2018)
Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset
Yung-Fu Chen et al.
JOURNAL OF HEALTHCARE ENGINEERING (2018)
A New Ensemble Classification System For Fracture Zone Prediction Using Imbalanced Micro-CT Bone Morphometrical Data
Vasileios Ch Korfiatis et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2018)
The New Possibilities from Big Data to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis
Christian Kruse
CURRENT OSTEOPOROSIS REPORTS (2018)
The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care
Matthieu Komorowski et al.
NATURE MEDICINE (2018)
Deep neural network improves fracture detection by clinicians
Robert Lindsey et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)
Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm
D. Devikanniga et al.
HEALTHCARE TECHNOLOGY LETTERS (2018)
Scalable and accurate deep learning with electronic health records
Alvin Rajkomar et al.
NPJ DIGITAL MEDICINE (2018)
Clinical fracture risk evaluated by hierarchical agglomerative clustering
C. Kruse et al.
OSTEOPOROSIS INTERNATIONAL (2017)
Anisotropic Discrete Dual-Tree Wavelet Transform for Improved Classification of Trabecular Bone
Hind Oulhaj et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2017)
A novel data preprocessing method for boosting neural network performance: A case study in osteoporosis prediction
Theodoros Iliou et al.
INFORMATION SCIENCES (2017)
Discrimination and Calibration of Clinical Prediction Models Users' Guides to the Medical Literature
Ana Carolina Alba et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)
Use of Trabecular Bone Score (TBS) as a Complementary Approach to Dual-energy X-ray Absorptiometry (DXA) for Fracture Risk Assessment in Clinical Practice
Enisa Shevroja et al.
JOURNAL OF CLINICAL DENSITOMETRY (2017)
A survey on deep learning in medical image analysis
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2017)
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva et al.
NATURE (2017)
Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images
Joseph E. Burns et al.
RADIOLOGY (2017)
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Stephen F. Weng et al.
PLOS ONE (2017)
Classification of the trabecular bone structure of osteoporotic patients using machine vision
Anushikha Singh et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2017)
Artificial intelligence for analyzing orthopedic trauma radiographs Deep learning algorithms-are they on par with humans for diagnosing fractures?
Jakub Olczak et al.
ACTA ORTHOPAEDICA (2017)
Strut analysis for osteoporosis detection model using dental panoramic radiography
Jae Joon Hwang et al.
DENTOMAXILLOFACIAL RADIOLOGY (2017)
Machine Learning Principles Can Improve Hip Fracture Prediction
Christian Kruse et al.
CALCIFIED TISSUE INTERNATIONAL (2017)
Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches
Niyazi Kilic et al.
JOURNAL OF MEDICAL SYSTEMS (2016)
A Survey of Predictive Modeling on Im balanced Domains
Paula Branco et al.
ACM COMPUTING SURVEYS (2016)
Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images
Lucas Frighetto-Pereira et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2016)
Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches
Muthu Subash Kavitha et al.
DENTOMAXILLOFACIAL RADIOLOGY (2016)
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)
Segmentation of joint and musculoskeletal tissue in the study of arthritis
Valentina Pedoia et al.
MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE (2016)
POINTS OF SIGNIFICANCE Model selection and overfitting
Jake Lever et al.
NATURE METHODS (2016)
Rayyan-a web and mobile app for systematic reviews
Mourad Ouzzani et al.
SYSTEMATIC REVIEWS (2016)
The accuracy of osteoporotic fracture risk prediction tools: a systematic review and meta-analysis
Andrea Marques et al.
ANNALS OF THE RHEUMATIC DISEASES (2015)
Evaluating Discrimination of Risk Prediction Models The C Statistic
Michael J. Pencina et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2015)
Fusion of clinical and stochastic finite element data for hip fracture risk prediction
Peng Jiang et al.
JOURNAL OF BIOMECHANICS (2015)
The precision recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases
Brice Ozenne et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2015)
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
Gary S. Collins et al.
BMJ-BRITISH MEDICAL JOURNAL (2015)
Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women
Muthu Subash Kavitha et al.
ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY (2015)
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
Gary S. Collins et al.
BMJ-BRITISH MEDICAL JOURNAL (2015)
Evaluation of low bone mineral mass using a combination of peripheral bone mineral density and total body composition variables by neural network
B. Ragini et al.
3RD INTERNATIONAL CONFERENCE ON RECENT TRENDS IN COMPUTING 2015 (ICRTC-2015) (2015)
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
Kenneth F. Schulz et al.
BMJ-BRITISH MEDICAL JOURNAL (2010)
The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration
Alessandro Liberati et al.
PLOS MEDICINE (2009)
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies
Erik von Elm et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2008)
An estimate of the worldwide prevalence and disability associated with osteoporotic fractures
O. Johnell et al.
OSTEOPOROSIS INTERNATIONAL (2006)
Assessment of fracture risk
JA Kanis et al.
OSTEOPOROSIS INTERNATIONAL (2005)
Share Paper
