A Novel Morphological Analysis of DXA-DICOM Images by Artificial Neural Networks for Estimating Bone Mineral Density in Health and Disease

Mohamed, Ehab I.; Meshref, Radwa A.; Abdel-Mageed, Samir M.; Moustafa, Moustafa H.; Badawi, Mohamed I.; Darwish, Samy H.

doi:10.1016/j.jocd.2018.08.006

Cited by 13 publications

(13 citation statements)

References 14 publications

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“…( 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. External validation was performed in two studies from different local institutions in the same country with comparable results.…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…( 23,32 ) 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. Of note, Pang and colleagues reported their methods in detail and shared the code used for their analysis.…”

Section: Resultsmentioning

confidence: 99%

“…( 22 ) We simplified the original MI‐CLAIM checklist by removing the points concerning direct clinical relevance and incorporating it within the study design part of the checklist. Based on this checklist, the most critical methodological and reporting limitations observed were presence of overfitting risk, ( 29,33,35,37,40,48,51,54,58–60,63,65 ) lack of model selection configuration reporting, ( 23–25,29,33,35,39,41,42,45,46,50,51,53,56–61,64,65 ) reporting of almost perfect model performances, ( 29,43,48,51,54,56,60,72,92 ) or lack of confidence intervals around point estimates. ( 33,34,44,52–65,72,73,86,88,92,102 ) In general, the studies where these issues were observed were not adequately acknowledged.…”

Section: Discussionmentioning

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

101

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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%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

101

View full text Add to dashboard Cite

show abstract

“…In contrast, fewer than 100 of these were in the field of osteoporosis, although this is following the same exponential trajectory with the majority of studies published during the last 2-3 years. Efforts have been made in osteoporosis diagnosis and classification, bone mineral density assessment, fracture detection, fracture risk estimation, and bone image segmentation [7][8][9][10][11][12][13][14]. The majority of these articles used opportunistic data-particularly in imaging.…”

mentioning

confidence: 99%