参考文献 References
[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.