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Ultrasound backscattered radiofrequency signals from the liver contain abundant information about its microstructure. We proposed a method for characterizing human hepatic fibrosis using one-dimensional convolutional neural networks (CNNs) based on ultrasound backscattered signals. The proposed CNN model was composed of four one-dimensional convolutional layers, four one-dimensional max-pooling layers, and four fully connected layers. Ultrasound radiofrequency signals collected from 230 participants (F0: 23; F1: 46; F2: 51; F3: 49; F4: 61) with a 3-MHz transducer were analyzed. Liver regions of interest (ROIs) that contained most of the liver ultrasound backscattered signals were manually delineated using B-mode images reconstructed from the backscattered signals. ROI signals were normalized and augmented by using a sliding window technique. After data augmentation, the radiofrequency signal segments were divided into training sets, validation sets and test sets at a ratio of 80%:10%:10%. In the test sets, the proposed algorithm produced an area under the receive operating characteristic curve of 0.933 (accuracy: 91.30%; sensitivity: 92.00%; specificity: 90.48%), 0.997 (accuracy: 94.29%; sensitivity: 94.74%; specificity: 93.75%), 0.818 (accuracy: 75.00%; sensitivity: 69.23%; specificity: 81.82%), and 0.934 (accuracy: 91.67%; sensitivity: 88.89%; specificity: 94.44%) for diagnosis liver fibrosis stage \u2265F1, \u2265F2, \u2265F3, and \u2265F4, respectively. Experimental results indicated that the proposed deep learning algorithm based on ultrasound backscattered signals yields a satisfying performance when diagnosing hepatic fibrosis stages. The proposed method may be used as a new quantitative ultrasound approach to characterizing hepatic fibrosis.<\/jats:p>","DOI":"10.3390\/diagnostics12112833","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T03:26:04Z","timestamp":1668655564000},"page":"2833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Evaluation of Hepatic Fibrosis Using Ultrasound Backscattered Radiofrequency Signals and One-Dimensional Convolutional Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Yong","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyu","family":"Bin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiying","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, BJUT Hospital, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuicai","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1054-1583","authenticated-orcid":false,"given":"Dar-In","family":"Tai","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan 333423, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Po-Hsiang","family":"Tsui","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan"},{"name":"Institute for Radiological Research, Chang Gung University, Taoyuan 333323, Taiwan"},{"name":"Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0570-8473","authenticated-orcid":false,"given":"Zhuhuang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.ultrasmedbio.2020.10.025","article-title":"Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver","volume":"47","author":"Nguyen","year":"2021","journal-title":"Ultrasound Med. 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