人工智能在肝再生评估及预测中的应用现状及前景

李汛, 宋晓静

PDF(772 KB)
PDF(772 KB)
西南医科大学学报 ›› 2025, Vol. 48 ›› Issue (1) : 1-5. DOI: 10.3969/j.issn.2096-3351.2025.01.001
专家述评

人工智能在肝再生评估及预测中的应用现状及前景

作者信息 +

Current Application Status and Prospects of Artificial Intelligence in Liver Regeneration Evaluation and Prediction

Author information +
History +

摘要

肝再生是肝脏维持正常生理功能及应对损伤的关键生物学过程,精准评估与预测肝再生状态对肝脏外科手术规划、肝病治疗策略制定及患者预后判断意义重大。本文系统评述人工智能(artificial intelligence, AI)在肝再生领域的应用现状,涵盖基于影像、临床数据及分子组学的评估手段,剖析当前面临的数据质量、算法可解释性等挑战,并展望多模态融合、实时动态监测及个性化医疗导向下 AI 的未来发展前景,为推动肝再生研究与临床实践革新提供参考。

Abstract

Liver regeneration is a key biological process for the liver to maintain normal physiological function and respond to injury. Accurately evaluating and predicting the state of liver regeneration is of great significance for liver surgery planning, liver disease treatment strategy formulation, and patient prognosis judgment. This article provided a systematic review of the current application status of artificial intelligence (AI) in the field of liver regeneration, covering evaluation methods based on imaging, clinical data, and molecular genomics. It analyzed the challenges currently faced in terms of data quality and algorithm interpretability, and looked forward to the future development prospects of AI under the guidance of multimodal fusion, real-time dynamic monitoring, and personalized medicine, providing reference for promoting innovation in liver regeneration research and clinical practice.

关键词

人工智能 / 肝再生 / 评估 / 预测

Key words

Artificial intelligence / Liver regeneration / Evaluation / Prediction

中图分类号

R605 / R575

引用本文

导出引用
李汛 , 宋晓静. 人工智能在肝再生评估及预测中的应用现状及前景. 西南医科大学学报. 2025, 48(1): 1-5 https://doi.org/10.3969/j.issn.2096-3351.2025.01.001
Xun LI, Xiaojing SONG. Current Application Status and Prospects of Artificial Intelligence in Liver Regeneration Evaluation and Prediction[J]. Journal of Southwest Medical University. 2025, 48(1): 1-5 https://doi.org/10.3969/j.issn.2096-3351.2025.01.001

参考文献

1
SHI JH, LINE PD. Hallmarks of postoperative liver regeneration: an updated insight on the regulatory mechanisms[J]. J Gastroenterol Hepatol, 2020, 35(6): 960-966.
2
GILGENKRANTZ H, DE L’HORTET AC. Understanding liver regeneration: from mechanisms to regenerative medicine[J]. Am J Pathol, 2018, 188(6): 1316-1327.
3
BHUSHAN B, APTE U. Liver regeneration after acetaminophen hepatotoxicity: mechanisms and therapeutic opportunities[J]. Am J Pathol, 2019, 189(4): 719-729.
4
POISSON J, LEMOINNE S, BOULANGER C, et al. Liver sinusoidal endothelial cells: Physiology and role in liver diseases[J]. J Hepatol, 2017, 66(1): 212-227.
5
MICHALOPOULOS GK, BHUSHAN B. Liver regeneration: biological and pathological mechanisms and implications[J]. Nat Rev Gastroenterol Hepatol, 2021, 18(1): 40-55.
6
CAMPANA L, ESSER H, HUCH M, et al. Liver regeneration and inflammation: from fundamental science to clinical applications[J]. Nat Rev Mol Cell Biol, 2021, 22(9): 608-624.
7
WANG FL, MA L, MOULTON G, et al. Clinician data scientists-preparing for the future of medicine in the digital world[J]. Health Data Sci, 2022, 2022: 9832564.
8
LIU CL, SOONG RS, LEE WC, et al. Predicting short-term survival after liver transplantation using machine learning[J]. Sci Rep, 2020, 10(1): 5654.
9
BALSANO C, BURRA P, DUVOUX C, et al. Artificial Intelligence and liver: Opportunities and barriers[J]. Dig Liver Dis, 2023, 55(11): 1455-1461.
10
SABANAYAGAM C, XU DJ, TING DSW, et al. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations[J]. Lancet Digit Health, 2020, 2(6): e295-e302.
11
LAU L, KANKANIGE Y, RUBINSTEIN B, et al. Machine-learning algorithms predict graft failure after liver transplantation[J]. Transplantation, 2017, 101(4): e125-e132.
12
REZAZADE MM, VAN O, HOMAN M. Applications of artificial intelligence (AI) in diagnostic radiology: a technography study[J]. Eur Radiol, 2021, 31(4): 1805-1811.
13
NAGAI SJ, NALLABASANNAGARI AR, MOONKA D, et al. Use of neural network models to predict liver transplantation waitlist mortality[J]. Liver Transpl, 2022, 28(7): 1133-1143.
14
BERTSIMAS D, KUNG J, TRICHAKIS N, et al. Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation[J]. Am J Transplant, 2019, 19(4): 1109-1118.
15
黄良江, 毛德文, 郑景辉, 等. 人工智能在肝性脑病风险预测模型中的应用进展[J]. 实用医学杂志, 2024, 40(3): 289-294.
16
KIEHL L, KUNTZ S, HÖHN J, et al. Deep learning can predict lymph node status directly from histology in colorectal cancer[J]. Eur J Cancer, 2021, 157: 464-473.
17
吴鹏, 高柳村, 汤善宏. 应用人工智能技术诊治肝脏疾病研究进展[J]. 实用肝脏病杂志, 2023, 26(2): 293-296.
18
张波, 张治英, 徐德忠, 等. 人工神经网络肝癌CT影像辅助诊断模型的建立[J]. 实用放射学杂志, 2006(9): 1079-1082.
19
张露, 丛冠宁, 杨小丽, 等. 多肿瘤标志物蛋白质芯片检测系统结合人工智能在肝癌诊断研究中的初步评价[J]. 中国医药导刊, 2003, 5(1): 35-37.
20
陈琦,冯露漪,贺松 等.人工智能技术在影像组学中的应用.数字化用户201925(46):129,135.
21
SON JH, LEE SS, LEE Y, et al. Assessment of liver fibrosis severity using computed tomography-based liver and spleen volumetric indices in patients with chronic liver disease[J]. Eur Radiol, 2020, 30(6): 3486-3496.
22
Feng C, Qiao C, Ji W, et al .In silico screening and in vivo experimental validation of 15-PGDH inhibitors from traditional Chinese medicine promoting liver regeneration. Int J Biol Macromol. 2024 Aug;274(Pt 1):133263.
23
AGRAWAL R, PRABAKARAN S. Big data in digital healthcare: lessons learnt and recommendations for general practice[J]. Heredity, 2020, 124(4): 525-534.
24
XIONG SH, FU YW, RAY A. Bayesian nonparametric modeling of categorical data for information fusion and causal inference[J]. Entropy (Basel), 2018, 20(6): 396.
25
PORUKALA M, VINOD PK. Systems-level analysis of transcriptome reorganization during liver regeneration[J]. Mol Omics, 2022, 18(4): 315-327.
26
BANGRU S, ARIF W, SEIMETZ J, et al. Alternative splicing rewires Hippo signaling pathway in hepatocytes to promote liver regeneration[J]. Nat Struct Mol Biol, 2018, 25(10): 928-939.
27
HONG LX, CAI YB, JIANG MT, et al. The Hippo signaling pathway in liver regeneration and tumorigenesis[J]. Acta Biochim Biophys Sin (Shanghai), 2015, 47(1): 46-52.
28
DOVMARK TH, KVIST PH, MØLCK AM, et al. Quantitative assessment of epithelial proliferation in rat mammary gland using artificial intelligence independent of choice of proliferation marker[J]. J Histochem Cytochem, 2022, 70(3): 237-250.
29
冯灿, 李江林, 曹锐, 等. 大鼠2/3肝切除72h后再生肝脏质膜比较蛋白质组学研究[J]. 中国生物化学与分子生物学报, 2012, 28(8): 751-760.
30
MAITI S, HASSAN A, MITRA P. Boosting phosphorylation site prediction with sequence feature-based machine learning[J]. Proteins, 2020, 88(2): 284-291.
31
LI FY, LI C, MARQUEZ-LAGO TT, et al. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome[J]. Bioinformatics, 2018, 34(24): 4223-4231.
32
AGNIHOTRY S, SARANGI AN, AGGARWAL R. Construction & assessment of a unified curated reference database for improving the taxonomic classification of bacteria using 16S rRNA sequence data[J]. Indian J Med Res, 2020, 151(1): 93-103.
33
LI LF, GUO JL, CHEN YH, et al. Comprehensive CircRNA expression profile and selection of key CircRNAs during priming phase of rat liver regeneration[J]. BMC Genomics, 2017, 18(1): 80.
34
RIGDEN DJ, GALPERIN MY, JEDRZEJAS MJ. Analysis of structure and function of putative surface-exposed proteins encoded in the Streptococcus pneumoniae genome: a bioinformatics-based approach to vaccine and drug design[J]. Crit Rev Biochem Mol Biol, 2003, 38(2): 143-168.
35
GREEN JJ, GRIMM C, FRISTO A, et al. Parsing 20 years of public data by AI maps trends in proteomics and forecasts technology[J]. J Proteome Res, 2024, 23(2): 523-531.
36
MORISHITA EC. Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies[J]. Expert Opin Drug Discov, 2023, 18(2): 207-226.
37
KUMAR P, SINHA R, SHUKLA P. Artificial intelligence and synthetic biology approaches for human gut microbiome[J]. Crit Rev Food Sci Nutr, 2022, 62(8): 2103-2121.
38
WANG S, YU H, GAN YC, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study[J]. Lancet Digit Health, 2022, 4(5): e309-e319.
39
ZHANG YW, LI PW, MA Y, et al. Artificial intelligence accelerates the mining of bioactive small molecules from human microbiome[J]. Clin Transl Med, 2022, 12(8): e1011.
40
LA PAGLIA L, VAZZANA M, MAURO M, et al. Bioactive molecules from the innate immunity of ascidians and innovative methods of drug discovery: a computational approach based on artificial intelligence[J]. Mar Drugs, 2023, 22(1): 6.
41
HUR MH, PARK MK, YIP TCF, et al. Personalized antiviral drug selection in patients with chronic hepatitis B using a machine learning model: a multinational study[J]. Am J Gastroenterol, 2023, 118(11): 1963-1972.
42
YUAN BH, LI RH, HUO RR, et al. Lower risk of hepatocellular carcinoma with tenofovir than entecavir treatment in subsets of chronic hepatitis B patients: an updated meta-analysis[J]. J Gastroenterol Hepatol, 2022, 37(5): 782-794.
43
张宁, 朱绍成. MRI征象预测肝细胞癌干细胞表型特征的研究进展[J]. 中华放射学杂志, 2024, 58(9): 972-976.
44
AATRESH AA, ALABHYA K, LAL S, et al. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images[J]. Int J Comput Assist Radiol Surg, 2021, 16(9): 1549-1563.
45
KHENED M, KORI A, RAJKUMAR H, et al. A generalized deep learning framework for whole-slide image segmentation and analysis[J]. Sci Rep, 2021, 11(1): 11579.
46
JIAO CZ, LAO Y, ZHANG WW, et al. Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction[J]. Phys Med Biol, 2024, 69(15): 10.1088/1361-10.1088/6560/ad64b8.
47
吴洪基, 王海霞, 汪玲, 等. 人工智能在类器官研究中的应用进展与挑战[J]. 中国癌症杂志, 2024, 34(2): 210-219.
48
ASRANI SK, DEVARBHAVI H, EATON J, et al. Burden of liver diseases in the world[J]. J Hepatol, 2019, 70(1): 151-171.
49
COLAK D, AL-HARAZI O, MUSTAFA OM, et al. RNA-seq transcriptome profiling in three liver regeneration models in rats: comparative analysis of partial hepatectomy, ALLPS, and PVL[J]. Sci Rep, 2020, 10(1): 5213.
50
韦英婷, 覃家盟, 樊金莲, 等. 基于深度学习算法开发和验证的肝细胞癌预后预测模型:一项大样本队列和外部验证研究. 中国癌症防治杂志, 2021, 13(3): 294-300.

基金

国家自然科学基金地区项目(82060119)
科技重大专项国际合作领域项目(23ZDWA003)
甘肃省普通外科临床医学研究中心项目(20JR10FA661)

评论

PDF(772 KB)

Accesses

Citation

Detail

段落导航
相关文章

/