Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network.
Objectives: The aim of this study was to develop a deep convolutional neural network (DCNN) for the prediction of the METAVIR score using B-mode ultrasonography images.Methods: Datasets from two tertiary academic referral centers were used. A total of 13,608 ultrasonography images from 3446 patients...
| Publicado en: | European Radiology Vol. 30; no. 2; pp. 1264 - 1274 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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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=141192301&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141192301 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Feb2020 vid: 30 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141192301 141192301 NLM31478087 141192301 10.1007/s00330-019-06407-1 NLM31478087 141192301 ppf: 1264 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. aug: au: Lee, Jeong Hyun Joo, Ijin Kang, Tae Wook Paik, Yong Han Sinn, Dong Hyun Ha, Sang Yun Kim, Kyunga Choi, Choonghwan Lee, Gunwoo Yi, Jonghyon Bang, Won-Chul affil: Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, 06351, Seoul, Republic of Korea sug: subj: Liver Cirrhosis Classification Liver Cirrhosis Reproducibility of Results ROC Curve Female Ultrasonography Male Clinical Competence Liver Cirrhosis Pathology Retrospective Design Algorithms Middle Age Funding Source Middle Aged: 45-64 years Female Male ab: Objectives: The aim of this study was to develop a deep convolutional neural network (DCNN) for the prediction of the METAVIR score using B-mode ultrasonography images.Methods: Datasets from two tertiary academic referral centers were used. A total of 13,608 ultrasonography images from 3446 patients who underwent surgical resection, biopsy, or transient elastography were used for training a DCNN for the prediction of the METAVIR score. Pathological specimens or estimated METAVIR scores derived from transient elastography were used as a reference standard. A four-class model (F0 vs. F1 vs. F23 vs. F4) was developed. Diagnostic performance of the algorithm was validated on a separate internal test set of 266 patients with 300 images and external test set of 572 patients with 1232 images. Performance in classification of cirrhosis was compared between the DCNN and five radiologists.Results: The accuracy of the four-class model was 83.5% and 76.4% on the internal and external test set, respectively. The area under the receiver operating characteristic curve (AUC) for classification of cirrhosis (F4) was 0.901 (95% confidence interval [CI], 0.865-0.937) on the internal test set and 0.857 (95% CI, 0.825-0.889) on the external test set, respectively. The AUC of the DCNN for classification of cirrhosis (0.857) was significantly higher than that of all five radiologists (AUC range, 0.656-0.816; p value < 0.05) using the external test set.Conclusions: The DCNN showed high accuracy for determining METAVIR score using ultrasonography images and achieved better performance than that of radiologists in the diagnosis of cirrhosis.Key Points: • DCNN accurately classified the ultrasonography images according to the METAVIR score. • The AUROC of this algorithm for cirrhosis assessment was significantly higher than that of radiologists. • DCNN using US images may offer an alternative tool for monitoring liver fibrosis. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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