Artificial intelligence for detecting and quantifying steatotic liver disease
Cuvinte cheie:
inteligenţă artificială, învățare profundă, învăţare automată, ficat gras, ultrasunete, steatoza hepaticăRezumat
The prevalence of hepatic steatosis is increasing globally. While non-invasive diagnostic methods like ultrasonography and clinical scoring systems have been suggested as alternatives to liver biopsy, their effectiveness has been questioned. Integrating Artificial Intelligence (AI) with traditional diagnostic methods is being explored to enhance the accuracy of non-invasive approaches. The research utilized science bibliographic databases for data retrieval, namely PubMed, Scopus, and Google Scholar. The search terms utilized were “fatty liver,” “hepatic steatosis” “artificial intelligent”, “machine learning”, “deep learning”, “convolutional neural network”, “artificial neural network” and “ultrasound” etc. The systematic review encompassed studies, which collectively demonstrated that AI had a notable impact on improving the diagnosis of various liver conditions including liver steatosis, steatohepatitis, liver fibrosis, and liver cirrhosis. Through qualitative analysis, it was found that AI was particularly effective in enhancing diagnostic accuracy for these conditions. The integration of AI-supported systems has shown promising advancements in the detection and quantification of steatosis, NASH, and liver fibrosis in patients with liver steatosis. These systems have demonstrated the ability to improve performance in accurately diagnosing and assessing the severity of liver diseases, providing healthcare professionals with valuable tools for more effective clinical management.
Referințe
1. AGARWAL, S., SHARMA, S., KUMAR, M., et al. Development of a machine learning model to predict bleed in esophageal varices in compensated advanced chronic liver disease: A proof of concept. In: J. Gastroenterol. Hepatol. 2021, nr. 36, pp. 2935-2942. https://doi.org/10.1111/jgh.15560
2. ALSWAT, K., ALJUMAH, A.A., SANAI, F.M., et al. Non-alcoholic fatty liver disease burden-Saudi Arabia and United Arab Emirates, 2017-2030. In: Saudi J. Gastroenterol. 2018, nr. 24 (4), pp. 211-219. https://doi.org/10.4103/sjg.SJG_122_18
3. ALTAMIRANO, J., MIQUEL, R., KATOONIZADEH, A., et al. A Histologic Scoring System for Prognosis of Patients with Alcoholic Hepatitis. In: Gastroenterology 2014, nr. 146, pp. 1231-1239.e6. https://doi.org/10.1053/j.gastro.2014.01.018
4. BEDOSSA, P., CARRAT, F. Liver biopsy: the best, not the gold standard. In: J. Hepatol. 2009, nr. 50(1), pp. 1-3. https://doi.org/10.1016/j.jhep.2008.10.014
5. BEDOSSA, P., POITOU, C., VEYRIE, N., et al. Histopathological algorithm and scoring system for evaluation of liver lesions in morbidly obese patients. In: Hepatology. 2012, nr. 56, pp.1751-1759. https://doi.org/10.1002/hep.25889
6. BEN-ISRAEL, I. Towards Regulation of AI Systems: Global Perspectives on the Development of a Legal Framework on Artificial Intelligence [AI] Systems Based on the Council of Europe's Standards on Human Rights, Democracy and the Rule of Law. Council of Europe. London, UK, 2020, 203p.
7. CHALASANI, N., YOUNOSSI, Z., LAVINE J.E., et al. The diagnosis and management of non-alcoholic fatty liver disease: practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. In: Hepatology. 2012, nr. 55, pp. 2005-2023. https://doi.org/10.1002/hep.25762
8. CHOI, KJ., JANG, JK., LEE, SS., et al. Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver. In: Radiology. 2018, nr. 289, pp. 688-697. https://doi.org/10.1148/radiol.2018180763
9. COLOM, R., KARAMA, S., JUNG, R.E., et al. Human intelligence and brain networks. In: Dialog. Clin. Neurosci. 2010, nr. 12, pp. 489-501. https://doi.org/10.31887/DCNS.2010.12.4/rcolom
10. DULAI, P.S., SINGH, S., PATEL, J., et al. Increased risk of mortality by fibrosis stage in nonalcoholic fatty liver disease: Systematic review and meta-analysis. In: Hepatology 2017, nr. 65, pp. 1557-1565. https://doi.org/10.1002/hep.29085
11. FERRAIOLI, G., ROCCARINA, D. Update on the role of elastography in liver disease. In: Therap. Adv. Gastroenterol. 2022, nr. 15, pp. 1-12. https://doi.org/10.1177/17562848221140657
12. FUKUDA, H., EBARA, M., KOBAYASHI, A., et al. An image analyzing system using an artificial neural network for evaluating the parenchymal echo pattern of cirrhotic liver and chronichepatitis. IEEE Trans. In Biomed. Eng. 1998, nr. 45, pp. 396-400. https://doi.org/10.1109/10.661164
13. FUKUDA, H., EBARA, M., KOBAYASHI, A., et al. Irregularity of parenchymal echo patterns of liver analyzed with a neural network and risk of hepatocellular carcinoma in liver cirrhosis. In: Oncology 2002, nr. 63, pp. 270-279. https://doi.org/10.1159/000065478
14. FUKUDA, H., EBARA, M., KOBAYASHI, A., et al. Parenchymal echo patterns of cirrhotic liver analysed with a neural network for risk of hepatocellular carcinoma. In: J. Gastroenterol. Hepatol. 1999, nr. 14, pp. 915-921. https://doi.org/10.1046/j.1440-1746.1999.01965.x
15. GE, J., KIM, WR., LAI JC., et al. "Beyond MELD" - emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation. In: J Hepatol. 2022, nr. 76, pp. 1318-29. https://doi.org/10.1016/j.jhep.2022.03.003
16. HÁJEK, M., DEZORTOVÁ, M., WAGNEROVÁ, D., et al. MR spectroscopy as a tool for in vivo determination of steatosis in liver transplant recipients. In: Magma (New York NY). 2011, nr. 24(5), pp. 297-304. https://doi.org/10.1007/s10334-011-0264-9
17. STUART, RJ., Norvig, P. Artificial intelligence: a modern approach. Pearson, 2021, 1127p.
18. HAN, A., BYRA, M., HEBA, E., et al. Noninvasive diagnosis of nonalcoholic fatty Liver Disease and quantification of Liver Fat with Radiofrequency Ultrasound Data using one-dimensional convolutional neural networks. In: Radiology. 2020, nr. 295(2), pp. 342-50. https://doi.org/10.1148/radiol.2020191160
19. HUANG, Y., LI, J., ZHENG, T., et al. Development and validation of a machine learning-based model for varices screening in compensated cirrhosis (CHESS2001): An international multicenter study. In: Gastrointest. Endosc. 2023, nr. 97, pp. 435-444.e2. https://doi.org/10.1016/j.gie.2022.10.018
20. KUPPILI, V., BISWAS, M., SREEKUMAR, A., et al. Extreme Learning Machine Framework for risk stratification of fatty Liver Disease using Ultrasound tissue characterization. In: J. Med Syst. 2017, nr. 41(10), p.152. https://doi.org/10.1007/s10916-017-0797-1
21. LECUN, Y., BENGIO, Y., HINTON, G. Deep learning. In: Nature 2015, nr. 521, pp. 436-444. https://doi.org/10.1038/nature14539
22. LI, J., WU, Q.Q., ZHU, R.H., et al. Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models. In: World J. Gastroenterol. 2022, nr. 28, pp. 4681-4697. https://doi.org/10.3748/wjg.v28.i32.4681
23. LI, J., ZOU, B., YEO, Y.H., et al. Prevalence, incidence, and outcome of non-alcoholic fatty liver disease in Asia, 1999-2019: A systematic review and meta-analysis. In: Lancet Gastroenterol. Hepatol. 2019, nr. 4, pp. 389-398. https://doi.org/10.1016/S2468-1253(19)30039-1
24. LIU, J.Q., REN, J.Y., XU, X.L., et al. Ultrasound-based artificial intelligence in gastroenterology and hepatology. In: World J. Gastroenterol. 2022, nr. 28, pp. 5530-5546. https://doi.org/10.3748/wjg.v28.i38.5530
25. MANTOVANI, A., SCORLETTI, E., MOSCA, A., et al. Complications, morbidity and mortality of nonalcoholic fatty liver disease. In: Metabolism 2020, nr. 111S, 154170. https://doi.org/10.1016/j.metabol.2020.154170
26. MASUZAKI, R., KANDA, T., SASAKI, R., et al. Application of artificial intelligence in hepatology: Mini review. Artif. Intell. In: Gastroenterol. 2020, nr. 1, pp. 5-11. https://doi.org/10.35712/aig.v1.i1.5
27. MIDDLETON, M.S., VAN NATTA, M.L., HEBA, E.R., et al. Diagnostic accuracy of magnetic resonance imaging hepatic proton density fat fraction in pediatric nonalcoholic fatty liver disease. In: Hepatology 2018, nr. 67, pp. 858-872. https://doi.org/10.1002/hep.29596
28. MOLLURA, D.J., CULP, M.P., POLLACK, E., et al. Artificial Intelligence in Low- and Middle-Income countries: Innovating Global Health Radiology. In: Radiology. 2020, nr. 297(3), pp. 513-20. https://doi.org/10.1148/radiol.2020201434
29. PAGE, M.J., MCKENZIE, J.E., BOSSUYT, P.M., et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. In: BMJ (Clinical Researched). 2021, nr. 372, p. n71.
30. PHISALPRAPA, P., SUPAKANKUNTI, S., CHARATCHAR-OENWIT THAYA, P., et al. Cost-effectiveness analysis of ultrasonography screening for nonalcoholic fatty Liver Disease in metabolic syndrome patients. In: Medicine. 2017, nr. 96(17), p. e6585. https://doi.org/10.1097/MD.0000000000006585
31. PICCININO, F., SAGNELLI, E., PASQUALE, G., et al. Complications following percutaneous liver biopsy: A multicentre retrospective study on 68 276 biopsies. In: J. Hepatol. 1986, nr. 2, pp. 165-173.
32. PROCOPET, B., CRISTEA, V.M., ROBIC, M.A., et al. Serum tests, liver stiffness and artificial neural networks for diagnosing cirrhosis and portal hypertension. In: Dig. Liver Dis. 2015, nr. 47, pp. 411-416. https://doi.org/10.1016/j.dld.2015.02.001
33. ROMERO-GÓMEZ, M., ZELBER-SAGI, S., TRENELL, M. Treatment of NAFLD with diet, physical activity and exercise. In: J. Hepatol. 2017, nr. 67, pp. 829-846. https://doi.org/10.1016/j.jhep.2017.05.016
34. UDOMPAP, P. THERNEAU, T.M., CANNING, R.E., et al. Performance of American Gastroenterological Association Clinical Care Pathway for the risk stratification of patients with nonalcoholic fatty liver disease in the US population. In: Hepatology 2023, nr. 77, pp. 931-941. https://doi.org/10.1002/hep.32739
35. WEI, W., WU, X., ZHOU, J. et al. Noninvasive evaluation of liver fibrosis reverse using artificial neural network model for chronic hepatitis B patients. In: Computer Math Methods Med. 2019, 2019:7239780. https://doi.org/10.1155/2019/7239780
36. WONG, RJ., AGUILAR, M., CHEUNG, R., et al. Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting liver transplantation in the United States. In: Gastroenterology. 2015, nr. 148, pp. 547-555. https://doi.org/10.1053/j.gastro.2014.11.039
37. WONG, V.W., CHAN, W.K., CHITTURI, S., et al. Asia-Pacific Working Party on Non-alcoholic Fatty Liver Disease guidelines 2017-Part 1: Definition, risk factors and assessment.22J. In: Gastroenterol. Hepatol. 2018, nr. 33, pp. 70-85. https://doi.org/10.1111/jgh.13857
38. YOUNOSSI, Z., TACKE, F., ARRESE, M. et al. Global Perspectives on Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis. In: Hepatology 2019, nr. 69, pp. 2672-2682. https://doi.org/10.1002/hep.30251
39. YU, Y., WANG, J., NG, C.W., et al. Deep learning enables automated scoring of liver fibrosis stages. In: Sci. Rep. 2018, nr. 8(1), 16016.
https://doi.org/10.1038/s41598-018-34300-2
40. ZHANG, L., LI, Q.Y., DUAN, Y.Y., et al. Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography. In: BMC Med. Inform. Decis. Mak. 2012, nr. 12 (55). https://doi.org/10.1186/1472-6947-12-55
41. ZHANG, Y., WENG, Y., LUND, J. Applications of explainable Artificial Intelligence in diagnosis and Surgery. In: Diagnostics (Basel Switzerland). 2022, nr. 12(2), 237. https://doi.org/10.3390/diagnostics12020237
42. ZHOU, L.Q., WANG, J.Y., YU, S.Y., et al. Artificial intelligence in medical imaging of the liver. In: World J. Gastroenterol. 2019, nr. 25, pp. 672-682. https://doi.org/10.3748/wjg.v25.i6.672
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