Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network

• Published in: European radiology Vol. 30; no. 2; pp. 1264 - 1273
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Biopsy; Cirrhosis; Classification; Confidence intervals; Deep learning; Diagnostic Radiology; Diagnostic systems; Fibrosis; Image classification; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Liver; Liver cirrhosis; Medical imaging; Medicine; Medicine & Public Health; Model accuracy; Neural networks; Neuroradiology; Radiology; Test methods; Ultrasonic imaging; Ultrasound; Deep learning; Liver; Ultrasonography; Fibrosis
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-019-06407-1

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.