Quantitative evaluation of liver fibrosis based on ultrasound radio frequency signals: An animal experimental study. (February 2021)

• Record Type: Journal Article
• Authors: Cheng, Guangwen; Dai, Meng; Xiao, Tianlei; Fu, Tiantian; Han, Hong; Wang, Yuanyuan; Wang, Wenping; Ding, Hong; Yu, Jinhua
• Is Part Of: Computer methods and programs in biomedicine. Volume 199(2021)
• Publication Date: 2021-02
• Subjects: Liver fibrosis -- Ultrasound radio frequency -- Deep learning -- Ultrasonography; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105875
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Quantitative evaluation of liver fibrosis based on ultrasound radio frequency signal. Bidirectional long short-term memory network designed for the data mining. Deep learning with radio frequency signal provides visual distribution of fibrosis. Abstract: Background: Chronic liver disease is an important cause of liver failure and death worldwide, and liver fibrosis is a common pathological process of most chronic liver diseases. There still lacks a useful tool for evaluating liver fibrosis progression precisely and non-invasively. The purpose of this study was to explore the use of ultrasound radio frequency (RF) signals combined with deep learning approach to evaluate the degree of liver fibrosis quantitatively. Methods: In this study, by extracting the output of deep learning models as a prediction value, a quantitative liver fibrosis prediction method was achieved based on the bidirectional long short-term memory (Bi-LSTM) network to analyze radio frequency (RF) signals. The dataset consisted of 160 sets of ultrasound RF signals of rat livers, including five fibrosis stages 0−4, upon pathological diagnosis. In total, 150 sets of RF signals were used to train four deep learning classification models, the output of which contained quantitative information. In each training stage of the four models, a large number of signal segments were extracted from the 150 sets and divided randomly into training and validation sets in a ratio of 80:20. Ten sets of RF data using the gold standard of quantitative fibrosis parameter (q-FP) of liver tissues were left for independent testing. To validate the proposed method, correlation analysis was carried out between q-FP and the quantitative prediction results based on the independent test data. Results: The accuracy of the four deep learning networks using the training and validation data was above 0.83 and 0.80, and the corresponding areas under the receiver operating characteristic curves were higher than 0.95 and 0.93, respectively. For the quantitative analysis in the independent test set, the determination coefficient, R 2, of the linear regression analysis between the quantitative prediction results and q-FP was above 0.93. liver fibrosis is a common pathological process of most chronic liver diseases. Conclusions: This study indicates that a prediction system based on ultrasound RF signals and a deep learning approach is promising for realizing quantitative and visualized diagnosis of liver fibrosis, which would be of great value in monitoring liver fibrosis non-invasively. Graphical abstract: The figure shows the results of quantitative and visual diagnosis of hepatic fibrosis by our deep learning network model. The pre-trained deep learning networks were applied to extract high-throughput features of the 10 sets of RF data in the independent test set and analyze them quantitatively, the overall fibrosis degrees were accurately predicted using a voting strategy, The prediction values of the RF segments are displayed on their corresponding ultrasound images in color, and their mean values are also given. The proposed method gives not only the overall decision of the liver fibrosis degree, but also the possibility of every fibrosis degree that the analyzed object may have in a pie chart. we could clearly see the distribution of predicted liver fibrosis in the areas of interest. The darker the color, the lower the fibrosis grade, and the lighter the color, the higher the liver fibrosis grade. Image, graphical abstract