New progress in the application of ultrasonic AI in viral hepatitis-related complications

doi:10.19871/j.cnki.xfcrbzz.2022.04.022

Abstract

Abstract: China has a high incidence of viral hepatitis, viral hepatitis can occur many complications, endanger the physical and mental health of patients, seriously threaten the lives of patients, early diagnosis of viral hepatitis-related complications is of great significance to guide clinical treatment.Artificial intelligence is becoming more and more popular in the field of medicine, and the research and application of ultrasonic artificial intelligence in the field of liver diseases is also deepening day by day. Ultrasonic artificial intelligence is a research hotspot in recent years to analyze the characteristics of clinical data and imaging data of a large number of patients with viral hepatitis and to establish various algorithm models to provide a new method of rapid diagnosis for clinic. This article reviews the new progress in the application of ultrasonic artificial intelligence in viral hepatitis-related complications.

Key words: Artificial intelligence, Viral hepatitis, Hepatic fibrosis, Liver cirrhosis, Focal hepatic lesions, Hepatocellular carcinoma

Zeng Weimei, Dong Changfeng. New progress in the application of ultrasonic AI in viral hepatitis-related complications[J]. Electronic Journal of Emerging Infectious Diseases, 2022, 7(4): 95-98.

References

[1] 何小敏, 咸建春. 关于《慢性乙型肝炎防治指南(2019年版)》的管见[J]. 中华传染病杂志, 2020,38(12):817-819.
[2] 中华医学会传染病与寄生虫病学分会肝病学分会. 病毒性肝炎防治方案[J]. 中华肝脏病杂志, 2000, 8(6):324-329.
[3] ANTEBY R, KLANG E, HORESH N, et al.Deep learning for noninvasive liver fibrosis classification: A systematic review[J]. Liver Int, 2021, 41(10):2269-2278.
[4] 雷丹, 梅英, 齐晓娅, 等. 人工智能在超声医学影像的应用研究进展[J]. 中国医疗器械信息, 2021, 27(7):42-43.
[5] BRIGANTI G, LE MOINE O.Artificial Intelligence in Medicine: Today and Tomorrow[J]. Front Med (Lausanne), 2020, 7:27.
[6] 王芳, 夏雨薇, 柴象飞, 等. 影像组学分析流程及临床应用的研究进展[J]. 中华解剖与临床杂志, 2021, 26(2):236-241.
[7] LECUN Y, BENGIO Y, HINTON G.Deep learning[J]. Nature, 2015,521(7553):436-444.
[8] 胡文墨, 杨华瑜, 毛一雷. 基于人工智能的影像组学在肝脏疾病中的应用[J]. 中华普通外科杂志, 2019,(7):646-648.
[9] BARNETT R.Liver cirrhosis[J]. Lancet, 2018, 392(10144): 275.
[10] Xue LY, Ding H.Current ultrasound-related strategies for assessing liver fibrosis in chronic liver disease[J]. Chin Med J (Engl), 2020, 133(22):2762-2764.
[11] 董常峰. 基于弹性成像多模态检测慢性乙型肝炎肝纤维化[J/CD]. 新发传染病电子杂志, 2018, 3(2):128.
[12] CASTERA L, PINZANI M.Non-invasive assessment of liver fibrosis: are we ready?[J]. Lancet, 2010, 375(9724):1419-1420.
[13] 宋家琳, 刘翔, 章建全, 等. 肝包膜超声图像几何特征定量评估肝纤维化的实验研究[J]. 中华超声影像学杂志, 2018, 27(12):1095-1099.
[14] WANG K, LU X, ZHOU H, et al.Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study[J]. Gut, 2019, 68(4):729-741.
[15] POYNARD T, BEDOSSA P, OPOLON P.Natural history of liver fibrosis progression in patients with chronic hepatitis C. The OBSVIRC, METAVIR, CLINIVIR, and DOSVIRC groups[J]. Lancet, 1997, 349(9055):825-832.
[16] XUE LY, JIANG ZY, FU TT, et al.Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis[J]. Eur Radiol, 2020, 30(5):2973-2983.
[17] 付甜甜, 姚钊, 丁红, 等. 计算机辅助诊断慢性乙肝患者肝纤维化进程的价值分析[J]. 中华医学杂志, 2019, 99(7):491-495.
[18] JEONG HL, IJIN J, TAE WK, et al.Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network[J]. European Radiology, 2020, 30(2):1264-1273.
[19] GAO LF, ZHOU RS, DONG CF, et al.Multi-Modal Active Learning For Automatic Liver Fibrosis Diagnosis Based On Ultrasound Shear Wave Elastography[C]. International Symposium on Biomedical Imaging, IEEE, 2021, 410-414.
[20] 中华人民共和国国家卫生健康委员会医政医管局. 原发性肝癌诊疗规范(2019年版)[J/CD]. 肝癌电子杂志, 2020, 7(1):5-23.
[21] 钟娴, 苏丽娅, 谢晓燕, 等. 肝局灶性病变边界和周围肝组织的弹性特征及其影响因素分析[J]. 中华超声影像学杂志, 2020, 29(4):349-353.
[22] 张琪, 董怡, 杨道辉, 等. 超声剪切波黏弹性技术在肝肿瘤良恶性鉴别中的初步临床应用[J]. 中华超声影像学杂志, 2019, 28(9):766-770.
[23] MITREA D, NEDEVSCHI S, BADEA R.Automatic Recognition of the Hepatocellular Carcinoma from Ultrasound Images using Complex Textural Microstructure Co-Occurrence Matrices (CTMCM)[C]. Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods, 2018.
[24] BREHAR R, MITREA DA, VANCEA F, et al.Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images[J]. Sensors (Basel), 2020, 20(11):3085.
[25] SCHMAUCH B, HERENT P, JEHANNO P, et al.Diagnosis of focal liver lesions from ultrasound using deep learning[J]. Diagn Interv Imaging, 2019, 100(4):227-233.
[26] YANG Q, WEI J, HAO X, et al.Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study[J]. EBio Medicine, 2020, 56:102777.
[27] DIETRICH CF, NOLSOE CP, BARR RG, et al.Guidelines and Good Clinical Practice Recommendations for Contrast Enhanced Ultrasound (CEUS) in the Liver - Update 2020 -WFUMB in Cooperation with EFSUMB, AFSUMB, AIUM, and FLAUS[J]. Ultraschall Med, 2020,41(5):562-585.
[28] QIN H, WU YQ, LIN P, et al.Ultrasound Image-Based Radiomics: An Innovative Method to Identify Primary Tumorous Sources of Liver Metastases[J]. J Ultrasound Med, 2021,40(6):1229-1244.
[29] WANG W, WU SS, ZHANG JC, et al.Preoperative Pathological Grading of Hepatocellular Carcinoma Using Ultrasomics of Contrast-Enhanced Ultrasound[J]. Acad Radiol, 2021, 28(8):1094-1101.
[30] LIU F, LIU D, WANG K, et al.Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients[J]. Liver Cancer, 2020, 9(4):397-413.