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Reviews in Endocrine and Metabolic Disorders

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15.08.2021 | Artificial Intelligence

Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review

verfasst von: Yifang Li, Xuetao Wang, Jun Zhang, Shanshan Zhang, Jian Jiao

Erschienen in: Reviews in Endocrine and Metabolic Disorders | Ausgabe 3/2022

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Abstract

Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.

Eslam M, Sanyal AJ, George J. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology 2020;158(7):1999–2014.e1991 [PMID: 32044314. https://doi.org/10.1053/j.gastro.2019.11.312].

Fouad Y, Waked I, Bollipo S, Gomaa A, Ajlouni Y, Attia D. What's in a name? Renaming 'NAFLD' to 'MAFLD'. Liver Int. 2020 [PMID: 32301554. https://doi.org/10.1111/liv.14478].

Zhang HJ, Wang YY, Chen C, Lu YL, Wang NJ. Cardiovascular and renal burdens of metabolic associated fatty liver disease from serial US national surveys, 1999-2016. Chin Med J (Engl). 2021 [PMID: 34091530. https://doi.org/10.1097/CM9.0000000000001513].

Sanyal AJ, Brunt EM, Kleiner DE, et al. Endpoints and clinical trial design for nonalcoholic steatohepatitis. Hepatology 2011;54(1):344–353 [PMID: 21520200 PMCID: PMC4014460. https://doi.org/10.1002/hep.24376].

Park JH, Koo BK, Kim W, Kim WH; Innovative Target Exploration of NAFLD (ITEN) Consortium. Histological severity of nonalcoholic fatty liver disease is associated with 10-year risk for atherosclerotic cardiovascular disease. Hepatol Int. 2021 [PMID: 34081289. https://doi.org/10.1007/s12072-021-10209-3].

Estes C, Anstee QM, Arias-Loste MT, et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016-2030. J Hepatol 2018;69(4):896–904 [PMID: 29886156. https://doi.org/10.1016/j.jhep.2018.05.036].

Zhu J, Xu D, Yang R, Liu M, Liu Y. The triglyceride glucose index (TyG) and CDKAL1 gene rs10946398 SNP are associated with NAFLD in Chinese adults. Minerva Endocrinol. 2020 [PMID: 33269568. https://doi.org/10.23736/S0391-1977.20.03273-3].

Wang S, Sun W, Cheng Y, Wang L, Ma S, Jing F, Zhang X, Zhou X. Relationship between plasma 12,13-diHOME level and nonalcoholic fatty liver disease in patients with type 2 diabetes and obesity. Minerva Endocrinol (Torino). 2021 [PMID: 33855386. https://doi.org/10.23736/S2724-6507.21.03424-6].

Castañé H, Baiges-Gaya G, Hernández-Aguilera A, Rodríguez-Tomàs E, Fernández-Arroyo S, Herrero P, Delpino-Rius A, Canela N, Menendez JA, Camps J, Joven J. Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview. Biomolecules. 2021;11(3):473 [PMID: 33810079; PMCID: PMC8004861. https://doi.org/10.3390/biom11030473].

10.

Deo RC. Machine Learning in Medicine. Circulation 2015;132(20):1920–1930 [PMID: 26572668 PMCID: PMC5831252. https://doi.org/10.1161/circulationaha.115.001593].

11.

Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine Learning and Data Mining Methods in Diabetes Research. Comput Struct Biotechnol J. 2017;15:104–116 [PMID: 28138367 PMCID: PMC5257026. https://doi.org/10.1016/j.csbj.2016.12.005].

12.

Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–619 [PMID: 30102808 DOI: https://doi.org/10.1111/joim.12822].

13.

Canbay A, Kälsch J, Neumann U, Rau M, Hohenester S, Baba HA, Rust C, Geier A, Heider D, Sowa JP. Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective. PLoS One. 2019;14(3):e0214436 [PMID: 30913263; PMCID: PMC6435145. https://doi.org/10.1371/journal.pone.0214436].

14.

Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol. 2021;36(3):543–550. [PMID: 33709607. https://doi.org/10.1111/jgh.15385].

15.

Dinani AM, Kowdley KV, Noureddin M. Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH- the State of the Art. Hepatology. 2021 [PMID: 33928671. https://doi.org/10.1002/hep.31869].

16.

Badillo S, Banfai B, Birzele F, Davydov II, Hutchinson L, Kam-Thong T, Siebourg-Polster J, Steiert B, Zhang JD. An Introduction to Machine Learning. Clin Pharmacol Ther. 2020;107(4):871–885 [PMID: 32128792; PMCID: PMC7189875. https://doi.org/10.1002/cpt.1796].

17.

Rizwan I, Haque I, Neubert J. Deep learning approaches to biomedical image segmentation. Inf Med Unlocked. 2020;18:100297.

18.

Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol. 2021;36(3):569–580 [PMID: 33709606. https://doi.org/10.1111/jgh.15415].

19.

Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. The application of artificial intelligence for the diagnosis and treatment of liver diseases. Hepatology. 2020 [PMID: 33098140.DOI: https://doi.org/10.1002/hep.31603].

20.

Vanderbeck S, Bockhorst J, Komorowski R, Kleiner DE, Gawrieh S. Automatic classification of white regions in liver biopsies by supervised machine learning. Hum Pathol. 2014;45(4):785–792 [PMID: 24565203 https://doi.org/10.1016/j.humpath.2013.11.011].

21.

Sowa JP, Heider D, Bechmann LP, Gerken G, Hoffmann D, Canbay A. Novel algorithm for non-invasive assessment of fibrosis in NAFLD. PLoS One 2013;8(4):e62439 [PMID: 23638085 PMCID: PMC3640062 https://doi.org/10.1371/journal.pone.0062439].

22.

Perveen S, Shahbaz M, Keshavjee K, Guergachi A. A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression. Sci Rep. 2018;8(1):2112 [PMID: 29391513 PMCID: PMC5794753. https://doi.org/10.1038/s41598-018-20166-x].

23.

Fialoke S, Malarstig A, Miller MR, Dumitriu A. Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients. AMIA Annu Symp Proc. 2018;2018:430–439 [PMID: 30815083 PMCID: PMC6371264].

24.

Cao W, An X, Cong L, Lyu C, Zhou Q, Guo R. Application of Deep Learning in Quantitative Analysis of 2-Dimensional Ultrasound Imaging of Nonalcoholic Fatty Liver Disease. J Ultrasound Med. 2020;39(1):51–59 [PMID: 31222786. https://doi.org/10.1002/jum.15070].

25.

Perakakis N, Polyzos SA, Yazdani A, et al. Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study. Metabolism 2019;101:154005 [PMID: 31711876. https://doi.org/10.1016/j.metabol.2019.154005].

26.

Garcia-Carretero R, Vigil-Medina L, Barquero-Perez O, Ramos-Lopez J. Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection. Metab Syndr Relat Disord. 2019;17(9):444–451 [PMID: 31675274. https://doi.org/10.1089/met.2019.0052].

27.

Gawrieh S, Sethunath D, Cummings OW, et al. Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD. Ann Diagn Pathol. 2020;47:151518 [PMID: 32531442. https://doi.org/10.1016/j.anndiagpath.2020.151518].

28.

Hwang A, Shi C, Zhu E, et al. Supervised learning reveals circulating biomarker levels diagnostic of hepatocellular carcinoma in a clinically relevant model of non-alcoholic steatohepatitis; An OAD to NASH. PLoS One 2018;13(6):e0198937 [PMID: 29944670 PMCID: PMC6019748 KJ, LP, SVG, DD, QA, MAY, IDG, PN. The specific roles of these authors are articulated in the 'author contributions' section. There are no patents, products in development or marketed products to declare. This does not alter our adherence to PLoS One policies on sharing data and materials. https://doi.org/10.1371/journal.pone.0198937].

29.

Ramot Y, Zandani G, Madar Z, Deshmukh S, Nyska A. Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice. Toxicol Pathol. 2020;48(5):702–707 [PMID: 32508268. https://doi.org/10.1177/0192623320926478].

30.

Forlano R, Mullish BH, Giannakeas N, et al. High-Throughput, Machine Learning-Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies From Patients With Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol. 2019 [PMID: 31887451. https://doi.org/10.1016/j.cgh.2019.12.025].

31.

Yip TC, Ma AJ, Wong VW, et al. Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population. Aliment Pharmacol Ther. 2017;46(4):447–456 [PMID: 28585725. https://doi.org/10.1111/apt.14172].

32.

Van Vleck TT, Chan L, Coca SG, et al. Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression. Int J Med Inform. 2019;129:334–341 [PMID: 31445275 PMCID: PMC6717556. https://doi.org/10.1016/j.ijmedinf.2019.06.028].

33.

Koleck TA, Dreisbach C, Bourne PE, Bakken S. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J Am Med Inform Assoc. 2019;26(4):364–379 [PMID: 30726935 PMCID: PMC6657282. https://doi.org/10.1093/jamia/ocy173].

34.

Wang K, Lu X, Zhou H, Gao Y, Zheng J, Tong M, Wu C, Liu C, Huang L, Jiang T, Meng F, Lu Y, Ai H, Xie XY, Yin LP, Liang P, Tian J, Zheng R. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut. 2019;68(4):729–741 [PMID: 29730602; PMCID: PMC6580779. https://doi.org/10.1136/gutjnl-2018-316204].

35.

Tang A, Destrempes F, Kazemirad S, Garcia-Duitama J, Nguyen BN, Cloutier G. Quantitative ultrasound and machine learning for assessment of steatohepatitis in a rat model. Eur Radiol. 2019;29(5):2175–2184 [PMID: 30560362. https://doi.org/10.1007/s00330-018-5915-z].

36.

Wu CC, Yeh WC, Hsu WD, Islam MM, Nguyen PAA, Poly TN, Wang YC, Yang HC, Jack Li YC. Prediction of fatty liver disease using machine learning algorithms. Comput Methods Programs Biomed. 2019;170:23–29 [PMID: 30712601. https://doi.org/10.1016/j.cmpb.2018.12.032].

37.

Che H, Brown LG, Foran DJ, Nosher JL, Hacihaliloglu I. Liver disease classification from ultrasound using multi-scale CNN. Int J Comput Assist Radiol Surg. 2021 [PMID: 34097226. https://doi.org/10.1007/s11548-021-02414-0].

38.

Corey KE, Kartoun U, Zheng H, Shaw SY. Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record. Dig Dis Sci. 2016;61(3):913–919 [PMID: 26537487 PMCID: PMC4761309. https://doi.org/10.1007/s10620-015-3952-x].

39.

Qu HQ, Li Q, Grove ML, et al. Population-based risk factors for elevated alanine aminotransferase in a South Texas Mexican-American population. Arch Med Res. 2012;43(6):482–488 [PMID: 22959976 PMCID: PMC3590902. https://doi.org/10.1016/j.arcmed.2012.08.005].

40.

Huo Y, Terry JG, Wang J, et al. Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations. Med Phys. 2019;46(8):3508–3519 [PMID: 31228267 PMCID: PMC6692233. https://doi.org/10.1002/mp.13675].

41.

Graffy PM, Sandfort V, Summers RM, Pickhardt PJ. Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment. Radiology 2019;293(2):334–342 [PMID: 31526254 PMCID: PMC6822771. https://doi.org/10.1148/radiol.2019190512].

42.

Redman JS, Natarajan Y, Hou JK, et al. Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing. Dig Dis Sci. 2017;62(10):2713–2718 [PMID: 28861720. https://doi.org/10.1007/s10620-017-4721-9].

43.

Forlano R, Mullish BH, Giannakeas N, Maurice JB, Angkathunyakul N, Lloyd J, Tzallas AT, Tsipouras M, Yee M, Thursz MR, Goldin RD, Manousou P. High-Throughput, Machine Learning-Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies From Patients With Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol. 2020;18(9):2081–2090.e9 [PMID: 31887451; PMCID: PMC7397508. https://doi.org/10.1016/j.cgh.2019.12.025].

44.

Neuschwander-Tetri BA, Loomba R, Sanyal AJ, et al. Farnesoid X nuclear receptor ligand obeticholic acid for non-cirrhotic, non-alcoholic steatohepatitis (FLINT): a multicentre, randomised, placebo-controlled trial. Lancet 2015; 385(9972): 956-965 [PMID: 25468160 PMCID: PMC4447192 DOI: https://doi.org/10.1016/s0140-6736(14)61933-4]

45.

Qu H, Minacapelli CD, Tait C, Gupta K, Bhurwal A, Catalano C, Dafalla R, Metaxas D, Rustgi VK. Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides. Comput Methods Programs Biomed. 2021;207:106153 [PMID: 34020377. https://doi.org/10.1016/j.cmpb.2021.106153].

46.

Feng G, Zheng KI, Li YY, Rios RS, Zhu PW, Pan XY, Li G, Ma HL, Tang LJ, Byrne CD, Targher G, He N, Mi M, Chen YP, Zheng MH. Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD. J Hepatobiliary Pancreat Sci. 2021 [PMID: 33908180. https://doi.org/10.1002/jhbp.972].

47.

Vanderbeck S, Bockhorst J, Kleiner D, Komorowski R, Chalasani N, Gawrieh S. Automatic quantification of lobular inflammation and hepatocyte ballooning in nonalcoholic fatty liver disease liver biopsies. Hum Pathol. 2015;46(5):767–775 [PMID: 25776030. https://doi.org/10.1016/j.humpath.2015.01.019].

48.

Douali N, Abdennour M, Sasso M, et al. Noninvasive diagnosis of nonalcoholic steatohepatitis disease based on clinical decision support system. Stud Health Technol Inform. 2013;192:1178 [PMID: 23920952].PubMed

49.

Ma H, Xu CF, Shen Z, Yu CH, Li YM. Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China. Biomed Res Int. 2018;2018:4304376 [PMID: 30402478 PMCID: PMC6192080. https://doi.org/10.1155/2018/4304376].

50.

Helal KM, Taylor JN, Cahyadi H, et al. Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease. FEBS Lett. 2019;593(18):2535–2544 [PMID: 31254349. https://doi.org/10.1002/1873-3468.13520].

51.

Katsiki N, Gastaldelli A, Mikhailidis DP. Predictive models with the use of omics and supervised machine learning to diagnose non-alcoholic fatty liver disease: A "non-invasive alternative" to liver biopsy? Metabolism 2019;101:154010 [PMID: 31711877 DOI: https://doi.org/10.1016/j.metabol.2019.154010].

52.

Chiappini F, Desterke C, Bertrand-Michel J, Guettier C, Le Naour F. Hepatic and serum lipid signatures specific to nonalcoholic steatohepatitis in murine models. Sci Rep. 2016;6:31587 [PMID: 27510159 PMCID: PMC4980672. https://doi.org/10.1038/srep31587].

53.

Canbay A, Kälsch J, Neumann U, et al. Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective. PLoS One 2019;14(3):e0214436 [PMID: 30913263 PMCID: PMC6435145 declare. JPS and AC state that they received royalties for a scientific lecture, which was in part supported by TECOmedical group. This does not alter the authors’ adherence to PLoS One policies on sharing data and materials. https://doi.org/10.1371/journal.pone.0214436].

54.

Brattain LJ, Telfer BA, Dhyani M, Grajo JR, Samir AE. Objective Liver Fibrosis Estimation from Shear Wave Elastography. Conf Proc IEEE Eng Med Biol Soc. 2018;2018:1–5 [PMID: 30440285. https://doi.org/10.1109/embc.2018.8513011].

55.

Berná G, Romero-Gomez M. The role of nutrition in non-alcoholic fatty liver disease: Pathophysiology and management. Liver Int. 2020;40 Suppl 1:102–108 [PMID: 32077594. https://doi.org/10.1111/liv.14360].

56.

Zeevi D, Korem T, Zmora N, et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell 2015;163(5):1079–1094 [PMID: 26590418. https://doi.org/10.1016/j.cell.2015.11.001].

57.

Segal E. Rich data sets could end costly drug discovery. Nature 2020;577(7792):S19 [PMID: 31996830. https://doi.org/10.1038/d41586-020-00200-7].

58.

Cesaretti M, Brustia R, Goumard C, Cauchy F, Poté N, Dondero F, Paugam-Burtz C, Durand F, Paradis V, Diaspro A, Mattos L, Scatton O, Soubrane O, Moccia S. Use of Artificial Intelligence as an Innovative Method for Liver Graft Macrosteatosis Assessment. Liver Transpl. 2020;26(10):1224–1232 [PMID: 32426934. https://doi.org/10.1002/lt.25801].

59.

Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47(8):856–860 [PMID: 26121088. https://doi.org/10.1038/ng.3314].

60.

Doan S, Maehara CK, Chaparro JD, Lu S, Liu R, Graham A, Berry E, Hsu CN, Kanegaye JT, Lloyd DD, Ohno-Machado L, Burns JC, Tremoulet AH; Pediatric Emergency Medicine Kawasaki Disease Research Group. Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes. Acad Emerg Med. 2016;23(5):628–36 [ PMID: 26826020. https://doi.org/10.1111/acem.12925].

61.

Xia J, Wang Z, Huan Y, et al. Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists. J Chem Inf Model. 2020;60(3):1202–1214 [PMID: 32050066. https://doi.org/10.1021/acs.jcim.9b01030].

62.

Yamada T, Iwasaki K, Maedera S, Ito K, Takeshima T, Noma H, Shojima N. Myocardial infarction in type 2 diabetes using sodium-glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning. Curr Med Res Opin. 2020;36(3):403–409 [PMID: 31855074. https://doi.org/10.1080/03007995.2019.1706043].

63.

Justice AC, Smith RV, Tate JP, McGinnis K, Xu K, Becker WC, Lee KY, Lynch K, Sun N, Concato J, Fiellin DA, Zhao H, Gelernter J, Kranzler HR. VA Million Veteran Program. AUDIT-C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations. Addiction. 2018;113(12):2214–2224 [PMID:29972609. https://doi.org/10.1111/add.14374].

64.

Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;9(4):e1312 [PMID: 32089788. https://doi.org/10.1002/widm.1312].

Titel: Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review
verfasst von: Yifang Li
Xuetao Wang
Jun Zhang
Shanshan Zhang
Jian Jiao
Publikationsdatum: 15.08.2021
Verlag: Springer US
Schlagwörter: Artificial Intelligence
Fatty Liver
Erschienen in: Reviews in Endocrine and Metabolic Disorders / Ausgabe 3/2022
Print ISSN: 1389-9155
Elektronische ISSN: 1573-2606
DOI: https://doi.org/10.1007/s11154-021-09681-x

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Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review

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