Classification of the trabecular bone structure of osteoporotic patients using machine vision

Singh, Anushikha; Dutta, Malay Kishore; Jennane, Rachid; Lespessailles, Éric

doi:10.1016/j.compbiomed.2017.10.011

Cited by 54 publications

(32 citation statements)

References 26 publications

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“…( 29,32–34,36–65 ) Osteoporosis classification was made based on lumbar BMD, ( 32–34,37,51 ) hip BMD, ( 38,50,58 ) lumbar and hip BMD, ( 29,39–42,46–48,53,59,60 ) other non‐standard assessments, ( 43,44,49,54–56,65 ) or unspecified. ( 36,45,52,57,61–64 ) Studies identified osteoporosis based on opportunistic imaging from CT, ( 32–34 ) X‐ray, ( 37,38,43–45,55–59,63,64 ) or dental imaging; (36,47–49,53,54,60,62 ) other studies used data from patient characteristics, ( 40,41,50,51,61,65 ) bone biomarkers, (29,39 ) or acoustical responses. ( 42,52 ) As outcome, studies classified osteoporotic versus normal patients, ( 29,36,39,40,43,49,50,52,54–57,62 ) osteoporotic versus non‐osteoporotic patients (based on a BMD T ‐score threshold of –2.5 SD), ( 34,38,44,64 ) normal versus abnormal subjects (based on the BMD T ‐score threshold of −1 SD), ( 33,41,42,45,47,48,58–60,65 ) experimented multiple classifications, ( 46,63 ) or assigned to three classes: osteoporosis (BMD T ‐score ≤ −2.5 SD), osteopenia (−2.5 < BMD T ‐score ≤ −1), and normal (BMD T ‐score > −1 SD).…”

Section: Resultsmentioning

confidence: 99%

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%

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

114

View full text Add to dashboard Cite

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“…Thus, discriminating between ASD and TC individuals is a challenging task. Classification Score (pvalue 0.0099) Luck Permutation scores Overall, the results of the present study suggest that a first-order statistical feature [7], such as the standard deviation of rs-fMRI time series extracted using Glasser parcellation may be a discriminating feature for the classification of a mental disorder like autism. In addition, projecting this statistical metric on an average structural graph can help discriminate ASD from TC subjects, as indicated by classification metrics.…”

Section: Supervised Classification Of Asdmentioning

confidence: 58%

“…On the other hand, several statistical features, such as the mean and the standard deviation (SD) of multivariate signals, have previously been used to compute a vector with discriminatory (spatial) features for disease classification [7].…”

Section: Introductionmentioning

confidence: 99%

Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging

Brahim

Farrugia

2020

Artificial Intelligence in Medicine

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Graph signal processing (GSP) is a framework that enables the generalization of signal processing to multivariate signals described on graphs. In this paper, we present an approach based on Graph Fourier Transform (GFT) and machine learning for the analysis of resting-state functional magnetic resonance imaging (rs-fMRI). For each subject, we use rs-fMRI time series to compute several descriptive statistics in regions of interest (ROI). Next, these measures are considered as signals on an averaged structural graph built using tractography of the white matter of the brain, defined using the same ROI. GFT of these signals is computed using the structural graph as a support, and the obtained feature vectors are subsequently benchmarked in a supervised learning setting. Further analysis suggests that GFT using structural connectivity as a graph and the standard deviation of fMRI time series as signals leads to more accurate supervised classification using a worldwide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange) when compared to several other statistical metrics. Moreover, the proposed approach outperforms several approaches, based on using functional connectomes or complex functional network measures as features for classification.

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“…While some studies used radiomics features such as texture analysis from CT or radiographs to predict the risk of fracture, three large cohort studies used ML to predict the risk of fracture in osteoporotic patients using different clinical parameters combined with quantitative dualenergy X-ray absorptiometry imaging results. [93][94][95][96][97][98][99] Another article used high-order radiomics features (fractals) extracted from radiographs to assess response to denosumab treatment in postmenopausal osteoporotic patients. 100 Although these studies illustrate promising applications of ML, some limitations need to be addressed before translating into clinical practice.…”

Section: Radiomics Literature: General Considerations and Musculoskelmentioning

confidence: 99%

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

Cuadra

Favre

Omoumi

2020

Semin Musculoskelet Radiol

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Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.

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Classification of the trabecular bone structure of osteoporotic patients using machine vision

Cited by 54 publications

References 26 publications

Machine Learning Solutions for Osteoporosis—A Review

Machine Learning Solutions for Osteoporosis—A Review

Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

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