机器学习在肝脏疾病诊疗中的进展-国际临床研究杂志-CSCIED科技核心评价数据库-手机版

机器学习在肝脏疾病诊疗中的进展

Advances in machine learning in the diagnosis and treatment of liver diseases

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DOI	10.12208/j.ijcr.20240348
刊名	国际临床研究杂志 International Journal of Clinical Research
年，卷(期)	2024, 8(9)
作者	王鑫鑫,郑盛,杨涓*,
作者单位	大理大学研究生部云南大理 ; 大理大学第二附属医院消化内科云南昆明 ;
摘要	随着医学技术的不断进步，机器学习（Machine Learning, ML）在医学领域的应用日益增加，尤其在肝脏病学方面展现了巨大的潜力。本文旨在通过回顾国内外相关研究，总结机器学习在肝脏疾病诊断和治疗中的现状和最新进展，并探讨不同机器学习算法如何提升诊断准确性、优化治疗方案以及改善预后评估。此外，我们还评估了机器学习在临床应用中的优势与挑战，并展望其未来的发展方向。
Abstract	As medical technology continues to advance, the application of machine learning (ML) in the field of hepatology is increasingly prominent. This paper aims to review relevant domestic and international research, summarize the current status and latest developments of machine learning in the diagnosis and treatment of liver diseases, and explore how different machine learning algorithms can enhance diagnostic accuracy, optimize treatment plans, and improve prognosis assessment. Additionally, we evaluate the advantages and challenges of machine learning in clinical applications while also providing an outlook on its future development direction.
关键词	机器学习；肝脏疾病；预测模型；精准医疗
KeyWord	Machine learning; Liver disease; Prediction model; Precision Medicine
基金项目
页码	42-47

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相关文献

[1] De Siervi S, Cannito S, Turato C. Chronic Liver Disease: Latest Research in Pathogenesis, Detection and Treatment
[J]. Int J Mol Sci. 2023;24(13):10633. Published 2023 Jun 25.
[2] Cao JS, Lu ZY, Chen MY, et al. Artificial intelligence in gastroenterology and hepatology: Status and challenges
[J]. World J Gastroenterol. 2021;27(16):1664-1690.
[3] Lee HW, Sung JJY, Ahn SH. Artificial intelligence in liver disease
[J]. J Gastroenterol Hepatol. 2021;36(3):539-542.
[4] Janiesch, C., Zschech, P. & Heinrich, K. Machine learning and deep learning
[J]. Electron Markets 31, 685–695 (2021).
[5] Rattan P, Penrice DD, Simonetto DA. Artificial Intelligence and Machine Learning: What You Always Wanted to Know but Were Afraid to Ask
[J]. Gastro Hep Adv. 2022;1(1):70-78.
[6] Hsieh C, Laguna A, Ikeda I, et al. Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma
[J]. Radiology. 2023;309(2):e222891.
[7] Survarachakan S, Prasad PJR, Naseem R, et al. Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions
[J]. Artif Intell Med. 2022;130:102331.
[8] Gao R, Zhao S, Aishanjiang K, et al. Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data
[J]. J Hematol Oncol. 2021;14(1):154.
[9] Xie Y, Chen S, Jia D, Li B, Zheng Y, Yu X. Artificial Intelligence-Based Feature Analysis of Ultrasound Images of Liver Fibrosis
[J] . Comput Intell Neurosci. 2022;2022:2859987.
[10] Wang K, Lu X, Zhou H, et al. Deep learning Radiomics of shear wave elastography Bleeding
[J]. Gastroenterology. 2020;158(1):160-167.
[11] Cheng S, Yu X, Chen X, et al. CT-based radiomics model for preoperative prediction of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt
[J]. Br J Radiol. 2022;95(1132):20210792.
[12] Fu S, Lai H, Huang M, et al. Multi-task deep learning network to predict future macrovascular invasion in hepatocellular carcinoma
[J]. EClinicalMedicine. 2021;42:101201.
[13] Liu SC, Lai J, Huang JY, et al. Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals
[J]. Cancer Imaging. 2021;21(1):56.
[14] He L, Li H, Dudley JA, et al. Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data
[J]. AJR Am J Roentgenol. 2019;213(3):592-601.
[15] 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.
[16] Mao B, Ma J, Duan S, Xia Y, Tao Y, Zhang L. Correction to: Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics
[J]. Eur Radiol. 2021;31(8):6407.
[17] Lee Y, Cappellato M, Di Camillo B. Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease
[J]. Gigascience. 2022;12:giad083.
[18] Al-Tashi Q, Saad MB, Muneer A, et al. Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review
[J]. Int J Mol Sci. 2023;24(9):7781.
[19] Zhang Z, Wang S, Zhu Z, Nie B. Identification of potential feature genes in non-alcoholic fatty liver disease using bioinformatics analysis and machine learning strategies
[J]. Comput Biol Med. 2023;157:106724.
[20] Niu, L. et al. Noninvasive proteomic biomarkers for alcohol-related liver disease
[J]. Nat. Med. 28, 1277–1287 (2022).
[21] Zhang ZM, Huang Y, Liu G, et al. Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma
[J]. Sci Rep. 2024;14(1):5274.
[22] Qin S, Hou X, Wen Y, et al. Machine learning classifiers for screening nonalcoholic fatty liver disease in general adults
[J]. Sci Rep. 2023;13(1):3638. Published 2023 Mar 3.
[23] Cheng N, Ren Y, Zhou J, et al. Deep Learning-Based Classification of Hepatocellular Nodular Lesions on Whole-Slide Histopathologic Images
[J]. Gastroenterology. 2022;162(7):1948-1961.e7.
[24] Moulaei K, Sharifi H, Bahaadinbeigy K, Haghdoost AA, Nasiri N. Machine learning for prediction of viral hepatitis: A systematic review and meta-analysis
[J]. Int J Med Inform. 2023;179:105243.
[25] Phan DV, Chan CL, Li AA, Chien TY, Nguyen VC. Liver cancer prediction in a viral hepatitis cohort: A deep learning approach
[J]. Int J Cancer. 2020;147(10):2871-2878.
[26] Xiao W, Huang X, Wang JH, et al. Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study
[J]. Lancet Digit Health. 2021;3(2):e88-e97.
[27] Calderaro J, Seraphin TP, Luedde T, Simon TG. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma
[J]. J Hepatol. 2022;76(6):1348-1361.
[28] Zeng Q, Klein C, Caruso S, et al. Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study
[J]. Lancet Oncol. 2023;24(12):1411-1422.
[29] Ji G W , Fan Y , Sun D W , et al. Machine Learning to Improve Prognosis Prediction of Early Hepatocellular Carcinoma After Surgical Resection
[J]. Journal of hepatocellular carcinoma, 8:913-923.
[30] Bo Z, Chen B, Zhao Z, et al. Prediction of Response to Lenvatinib Monotherapy for Unresectable Hepatocellular Carcinoma by Machine Learning Radiomics: A Multicenter Cohort Study
[J]. Clin Cancer Res. 2023;29(9):1730-1740.
[31] Shah, N. R., Khetpal, V. & Erqou, S. Anticipating and addressing challenges during implementation of clinical decision support systems
[J]. JAMA Netw. Open 5,e2146528 (2022).
[32] Chen D, Liu J, Zang L, et al. Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients
[J]. Int J Biol Sci. 2022;18(1):360-373. Published 2022 Jan 1.
[33] Taylor-Weiner A, Pokkalla H, Han L, et al. A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH
[J]. Hepatology. 2021;74(1):133-147.
[34] Choi GH, Yun J, Choi J, et al. Development of machine learning-based clinical decision support system for hepatocellular carcinoma
[J]. Sci Rep. 2020;10(1):14855.
[35] Lee KH, Choi GH, Yun J, et al. Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study
[J]. NPJ Digit Med. 2024;7(1):2. Published 2024 Jan 5.
[36] Bao H, Li J, Zhang B, Huang J, Su D, Liu L. Integrated bioinformatics and machine-learning screening for immune-related genes in diagnosing non-alcoholic fatty liver disease with ischemic stroke and RRS1 pan-cancer analysis
[J]. Front Immunol. 2023;14:1113634.
[37] Kucukkaya AS, Zeevi T, Chai NX, et al. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
[J]. Sci Rep. 2023;13(1):7579.
[38] Zeng J, Zeng J, Lin K, et al. Development of a machine learning model to predict early recurrence for hepatocellular carcinoma after curative resection
[J]. Hepatobiliary Surg Nutr. 2022;11(2):176-187.
[39] Pu L, Sun Y, Pu C, et al. Machine learning-based disulfidptosis-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity in hepatocellular carcinoma
[J]. Sci Rep. 2024;14(1):4354. Published 2024 Feb 22.
[40] Chen C, Wang C, Li Y, Jiang S, Yu N, Zhou G. Prognosis and chemotherapy drug sensitivity in liver hepatocellular carcinoma through a disulfidptosis-related lncRNA signature
[J]. Sci Rep. 2024;14(1):7157.
[41] Dong B, Zhang H, Duan Y, Yao S, Chen Y, Zhang C. Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma
[J]. J Transl Med. 2024;22(1):455.
[42] Saillard C, Schmauch B, Laifa O, et al. Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides
[J]. Hepatology. 2020; 72(6):2000-2013.
[43] Spann A, Yasodhara A, Kang J, et al. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review
[J]. Hepatology. 2020;71(3):1093-1105.
[44] Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine
[J]. Nat Med. 2022;28(1):31-38.
[45] Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare
[J]. Br Med Bull. 2021;139(1):4-15.
[46] Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare
[J]. Nat Biomed Eng. 2018;2(10):719-731.
[47] Polevikov S. Advancing AI in healthcare: A comprehensive review of best practices
[J]. Clin Chim Acta. 2023; 548:117519.

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王鑫鑫,郑盛,杨涓*. 机器学习在肝脏疾病诊疗中的进展 [J]. 国际临床研究杂志. 2024; 8; (9). 42 - 47.

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