Classification of Osteoporosis by Extracting the Microarchitectural Properties of Trabecular Bone from DXA Scans Based on Thresholding Technique

Lee, Ju Hwan; Kim, Ga Young; Hwang, Yoo Na; Park, Sung Yun; Kim, Sung Min

doi:10.1166/jmihi.2015.1645

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…A 2019 review article reported that recent developments in AI have led to successful applications aiding osteoporosis diagnosis [ 15 ]. The following modalities have been used: dental radiographs [ 16 , 17 ], spine radiographs [ 14 , 18 ], hand and wrist radiographs [ 19 , 20 , 21 , 22 ], DXA imaging [ 23 , 24 ], and spine computed tomography [ 25 , 26 ]. However, two reports are available on osteoporosis diagnosis from hip radiographs using machine learning [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

Yamamoto

Sukegawa

Kitamura³

et al. 2020

Biomolecules

111

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This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

Yamamoto

Sukegawa

Kitamura³

et al. 2020

Biomolecules

111

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“…Not all of the image-based features are helpful for tissue characterization, and some may negatively influence the classification results [48, 49]. In order to reduce the dimensionalities and select optimum feature sets, principal component analysis (PCA) was implemented followed by a varimax rotation.…”

Section: Methodsmentioning

confidence: 99%

Automated classification of dense calcium tissues in gray-scale intravascular ultrasound images using a deep belief network

et al. 2019

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BackgroundIVUS is widely used to quantitatively assess coronary artery disease. The purpose of this study was to automatically characterize dense calcium (DC) tissue in the gray scale intravascular ultrasound (IVUS) images using the image textural features.MethodsA total of 316 Gy-scale IVUS and corresponding virtual histology images from 26 patients with acute coronary syndrome who underwent IVUS along with X-ray angiography between October 2009 to September 2014 were retrospectively acquired and analyzed. One expert performed all procedures and assessed their IVUS scans. After image acquisition, the DC candidate and corresponding acoustic shadow regions were automatically determined. Then, nine image-base feature groups were extracted from the DC candidates. In order to reduce the dimensionalities, principal component analysis (PCA) was performed, and selected feature sets were utilized as an input for a deep belief network. Classification results were validated using 10-fold cross validation.ResultsThe dimensionality of the feature map was efficiently reduced by 50% (from 66 to 33) without any performance decrease using PCA method. Sensitivity, specificity, and accuracy of the proposed method were 92.8 ± 0.1%, 85.1 ± 0.1%, and 88.4 ± 0.1%, respectively (p < 0.05). We found that the window size could largely influence the characterization results, and selected the 5 × 5 size as the best condition. We also validated the performance superiority of the proposed method with traditional classification methods.ConclusionsThese experimental results suggest that the proposed method has significant clinical applicability for IVUS-based cardiovascular diagnosis.

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Convolutional Neural Network Model based Deep Learning Approach for Osteoporosis Fracture Detection

Dhanalakshmi

Thenmozhi

Saxena³

et al. 2023

2023 International Conference on Networking and Communications (ICNWC)

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Classification of Osteoporosis by Extracting the Microarchitectural Properties of Trabecular Bone from DXA Scans Based on Thresholding Technique

Cited by 3 publications

References 23 publications

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

Automated classification of dense calcium tissues in gray-scale intravascular ultrasound images using a deep belief network

Convolutional Neural Network Model based Deep Learning Approach for Osteoporosis Fracture Detection

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