Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network.
Objectives: To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images.Methods: In this Institutional Review Board-approved retrospective study, patients who received both unenhanced CT examina...
| Publicado en: | European Radiology Vol. 30; no. 6; pp. 3549 - 3558 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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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=143397302&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143397302 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2020 vid: 30 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143397302 143397302 NLM32060712 143397302 10.1007/s00330-020-06677-0 NLM32060712 143397302 ppf: 3549 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. aug: au: Yasaka, Koichiro Akai, Hiroyuki Kunimatsu, Akira Kiryu, Shigeru Abe, Osamu affil: Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, 108-8639, Tokyo, Japan sug: subj: Tomography, X-Ray Computed Lumbar Vertebrae Osteoporosis Bone Density Absorptiometry, Photon Adult Female Abdomen Reproducibility of Results ROC Curve Middle Age Pharmacokinetics Aged Retrospective Design Male Funding Source Human Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Objectives: To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images.Methods: In this Institutional Review Board-approved retrospective study, patients who received both unenhanced CT examinations and dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae, in two institutions (1 and 2), were included. Supervised deep learning was employed to obtain a convolutional neural network (CNN) model using axial CT images, including the lumbar vertebrae as input data and BMD values obtained with DXA as reference data. For this purpose, 1665 CT images from 183 patients in institution 1, which were augmented to 99,900 (= 1665 × 60) images (noise adding, parallel shift and rotation were performed), were used. Internal (by using data of 45 other patients in institution 1) and external validations (by using data of 50 patients in institution 2) were performed to evaluate the performance of the trained CNN model. Correlations and diagnostic performances were evaluated with Pearson's correlation coefficient (r) and area under the receiver operating characteristic curve (AUC), respectively.Results: The estimated BMD values, according to the CNN model (BMDCNN), were significantly correlated with the BMD values obtained with DXA (r = 0.852 (p < 0.001) and 0.840 (p < 0.001) for the internal and external validation datasets, respectively). Using BMDCNN, osteoporosis was diagnosed with AUCs of 0.965 and 0.970 for the internal and external validation datasets, respectively.Conclusions: Using deep learning, the BMD of lumbar vertebrae could be predicted from unenhanced abdominal CT images.Key Points: • By applying a deep learning technique, the bone mineral density (BMD) of lumbar vertebrae can be estimated from unenhanced abdominal CT images. • A strong correlation was observed between the estimated BMD and the BMD obtained with DXA. • By using the estimated BMD, osteoporosis could be diagnosed with high performance. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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