Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

Fang, Yijie; Li, Wei; Chen, Xiaojun; Chen, Keming; Han, Kang; Yu, Pengxin; Zhang, Rongguo; Liao, Jianwei; Hong, Guobin; Li, Shaolin

doi:10.1007/s00330-020-07312-8

Cited by 74 publications

(69 citation statements)

References 35 publications

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“…Thirteen studies investigated bone properties such as vertebral fracture load, ( 23–25 ) microarchitecture parameters, ( 26,27 ) vertebral height, ( 28 ) or BMD ( 29–35 ) (Table 1). The main objective of these efforts was to improve the diagnosis of osteoporosis.…”

Section: Resultsmentioning

confidence: 99%

“…( 23 ) Microarchitecture parameters were determined using simulations or data collected from human cadavers. ( 26,27 ) Among the BMD studies, five assessed vertebral BMD, ( 30–34 ) one vertebral and hip BMD, ( 29 ) and one total body BMD. ( 35 ) Internal validation was performed in each study.…”

Section: Resultsmentioning

confidence: 99%

“…External validation was performed in two studies from different local institutions in the same country with comparable results. ( 23,32 ) Feature selection, ( 23 ) data augmentation, (32 ) and transfer learning from pre‐trained models ( 28,30 ) were applied to control overfitting. However, critical limitations in the use of ML methods were present: the gold standard, BMD as estimated from DXA, was not used as the ground truth (BMD from DXA is the reference technique used in practice, whereas BMD as estimated from CT scans was used as the ground truth); (30–32,34 ) model parameter selection was not reported; (23–25,29,33,35 ) discussion of crucial limitations regarding generalization was absent; (24,25,27,28,32,35 ) and the inclusion of the training set in model evaluation ( 33 ) or the use of 25 times more input features than data, ( 35 ) both indicating an increased risk of overfitting.…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

114

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Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high‐dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12‐point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML‐based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model‐based decision‐making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

114

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show abstract

“…Opportunistic screening for osteoporosis using other imaging modalities has been assessed previously. The best studied strategy is the use of abdominal CT to predict BMD; 13,32,33 classify osteoporosis based on CT attenuation, 12 simulated BMD, 32, 33 T-score, 13 or detection of osteoporotic fractures; 34 or use imaging biomarkers to predict the risk of fractures. 14 An earlier study compared the CT Hounsfield units over a manually annotated ROI involving vertebral body trabecular bone with its paired DXA T-score; this approach for detection of osteoporosis yielded an AUC of 0.83.…”

Section: Discussionmentioning

confidence: 99%

“…12 A deep learning-based model provided a better correlation between predicted and reference values, but its validation included only small datasets. 13,32,33 A larger study testing the performance of simulated T-scores on a larger dataset of 1843 CT-DXA pairs achieved an accuracy of 82% to detect osteoporosis. 13 This algorithm was integrated with VCF identification and CT trabecular density as biomarkers, and its performance for the prediction of 5-year fracture risks was compared with the performance of FRAX alone (i.e., without BMD input).…”

Section: Discussionmentioning

confidence: 99%

Automated and Precise Bone Mineral Density Prediction and Fracture Risk Assessment using Hip/Lumbar Spine Plain Radiographs via Learning Deep Image Signatures and Correlations

Hsieh

Zheng²,

Lin

et al. 2021

Preprint

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Dual-energy X-ray absorptiometry (DXA) and the Fracture Risk Assessment Tool are recommended tools for osteoporotic fracture risk evaluation, but are underutilized. We present a novel and fully-automated tool to identify fractures, predict bone mineral density (BMD), and evaluate fracture risk using plain pelvis and lumbar spine radiographs. The performance of this tool were evaluated in 1639 and 11908 patients with pelvis or lumbar spine radiographs and DXA, respectively. The model was well calibrated for hip and spine BMD assessments with minimal or no bias. The area under the curve and accuracy were 0.89 and 92.4% for hip osteoporosis, 0.87 and 86.8% for spine osteoporosis, 0.92 and 94.6% for high 10-year major fracture risk, and 0.92 and 92.2% for high hip fracture risk, respectively. The success rates of our automated algorithm a real-world test were 85.3% and 90.4% for hip and spine, respectively. The clinical use of this automated tool may increase the likelihood of identifying high-risk patients in previously unscreened populations.

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Treatment of Osteoporosis and the Use of Digital Health Intervention

2024

Artificial Intelligence for Bone Disorder

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Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

Cited by 74 publications

References 35 publications

Machine Learning Solutions for Osteoporosis—A Review

Machine Learning Solutions for Osteoporosis—A Review

Automated and Precise Bone Mineral Density Prediction and Fracture Risk Assessment using Hip/Lumbar Spine Plain Radiographs via Learning Deep Image Signatures and Correlations

Treatment of Osteoporosis and the Use of Digital Health Intervention

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