The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population.
Objective: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray features extracted by deep learning to alert high-risk...
| Publicado en: | Skeletal Radiology Vol. 49; no. 4; pp. 613 - 619 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141728444&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141728444 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03642348 O14 jtl: Skeletal Radiology issn: 03642348 maglogo: N pubinfo: dt: Apr2020 vid: 49 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141728444 141728444 NLM31760458 10.1007/s00256-019-03342-6 NLM31760458 141728444 ppf: 613 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population. aug: au: Lee, Sangwoo Choe, Eun Kyung Kang, Hae Yeon Yoon, Ji Won Kim, Hua Sun affil: Samsung Electronics, Suwon, South Korea sug: subj: Radiographic Image Interpretation, Computer-Assisted Methods Radiography Methods Osteoporosis Spine Bone Density Female South Korea Absorptiometry, Photon Methods Middle Age Male Retrospective Design Sensitivity and Specificity Predictive Value of Tests Middle Aged: 45-64 years Female Male ab: Objective: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray features extracted by deep learning to alert high-risk osteoporosis populations.Materials and Methods: We retrospectively used data obtained from health check-ups including spine X-ray and dual-energy X-ray absorptiometry (DXA). Consecutively, we selected people with normal and abnormal bone mineral density. From the regions of interest of X-ray images, deep convolutional networks were used to generate image features. We designed prediction models for abnormal BMD using the image features trained by machine learning classification algorithms. The performances of each model were evaluated.Results: From 334 participants, 170 images of abnormal (T scores < - 1.0 standard deviations (SD)) and 164 of normal BMD (T scores > = - 1.0 SD) were used for analysis. We found that a combination of feature extraction by VGGnet and classification by random forest based on the maximum balanced classification rate (BCR) yielded the best performance in terms of the area under the curve (AUC) (0.74), accuracy (0.71), sensitivity (0.81), specificity (0.60), BCR (0.70), and F1-score (0.73).Conclusion: In this study, we explored various machine learning algorithms for the prediction of BMD using simple spine X-ray image features extracted by three deep learning algorithms. We identified the combination for the best performance in predicting high-risk populations with abnormal BMD. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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