Artificial Intelligence in Predicting Risks of Surgical Interventions in Patients with Liver Cirrhosis

Aim: to develop neural network predictive model for the risk of postoperative complications and mortality in patients with liver cirrhosis undergoing minimally invasive surgical interventions.

Material and methods. Surgical interventions were performed on 90 patients with liver cirrhosis to correct complications of portal hypertension (ligation of esophageal varices (n = 57), transjugular intrahepatic portosystemic shunting (n = 6)) and for surgical treatment of comorbid conditions (n = 30). Two operations were performed on three patients during a single hospitalization. Mortality was 2.2 %, and postoperative complications were identified in 16 (17.8 %) individuals. For all patients, internationally recognized scales developed for patients with liver cirrhosis were calculated and included in the predictive model: Child — Turcotte — Pugh, MELD, Mayo Postoperative Surgical Risk Score, and VOCAL-Penn. Using automated neural networks and the Data Mining package in Statistica, comprehensive models for predicting surgical complications and mortality were developed.

Results. Comprehensive predictive models were created, incorporating the assessment of clinical, biochemical parameters, and quality of life indicators, which possess high predictive value. Based on these models, two calculators were proposed for calculating the risk of postoperative complications and mortality in patients with liver cirrhosis.

Conclusion. The integration of minimally invasive technologies for correcting complications of portal hypertension and predictive models developed through machine learning opens new possibilities for improving the outcomes of surgical interventions in patients with liver cirrhosis.

1. Ministry of Health of the Russian Federation. Clinical guidelines. Liver cirrhosis and fibrosis. 2023. (In Russ.).

2. Huang D.Q., Terrault N.A., Tacke F., Gluud L.L., Arrese M., Bugianesi E., et al. Global epidemiology of cirrhosis — aetiology, trends and predictions. Nat Rev Gastroenterol Hepatol. 2023;20(6):388–98. DOI: 10.1038/s41575-023-00759-2

3. Liu Y.B., Chen M.K. Epidemiology of liver cirrhosis and associated complications: Current knowledge and future directions. World J Gastroenterol. 2022;28(41):5910–30. DOI: 10.3748/wjg.v28.i41.5910

4. Bakulin I.G., Oganezova I.A., Skalinskaya M.I., Skazyvaeva E.V. Liver cirrhosis and complication risk management. Terapevticheskii Arkhiv. 2021;93(8):963–8. (In Russ.). DOI: 10.26442/00403660.2021.08.200917

5. Kaplan D.E., Bosch J., Ripoll C., Thiele M., Fortune B.E., Simonetto D.A., et al. AASLD practice guidance on risk stratification and management of portal hypertension and varices in cirrhosis. Hepatology. 2023;79(5):1180–211. DOI: 10.1097/HEP.0000000000000647

6. Kim W.R., Mannalithara A., Heimbach J.K., Kamath P.S., Asrani S.K., Biggins S.W., et al. MELD 3.0: The model for end-stage liver disease updated for the modern era. Gastroenterology. 2021;161(6):1887–95. DOI: 10.1053/j.gastro.2021.08.050

7. Jadaun S.S., Saigal S. Surgical risk assessment in patients with chronic liver diseases. J Clin Exp Hepatol. 2022;12(4):1175–83. DOI: 10.1016/j.jceh.2022.03.004

8. Teh S.H., Nagorney D.M., Stevens S.R., Offord K.P., Therneau T.M., Plevak D.J., et al. Risk factors for mortality after surgery in patients with cirrhosis. Gastroenterology. 2007;132(4):1261–9. DOI: 10.1053/j.gastro.2007.01.040

9. Mahmud N., Fricker Z., Hubbard R.A., Ioannou G.N., Lewis J.D., Taddei T.H., et al. Risk prediction models for post-operative mortality in patients with cirrhosis. Hepatology. 2021;73(1):204–18. DOI: 10.1002/hep.31558

10. Mahmud N., Fricker Z., Panchal S., Lewis J.D., Goldberg D.S., Kaplan D.E. External validation of the VOCALPenn cirrhosis surgical risk score in 2 large, independent health systems. Liver Transpl. 2021;27(7):961–70. DOI: 10.1002/lt.26060

11. Canillas L., Pelegrina A., Colominas-González E., Salis A., Enríquez-Rodríguez C.J., Duran X. Comparison of surgical risk scores in a European cohort of patients with advanced chronic liver disease. J Clin Med. 2023;12(18):6100. DOI: 10.3390/jcm12186100

12. Chang J., Hoffstall S., Gödiker J., Lehmann J., Schwind L., Lingohr P., et al. Surgical site infections are independently associated with the development of postoperative acuteon-chronic liver failure in liver cirrhosis. Liver Transpl. 2023;29(9):928–39. DOI: 10.1097/LVT.0000000000000135

13. Ostojic A., Mahmud N., Reddy K.R. Surgical risk stratification in patients with cirrhosis. Hepatol Int. 2024;18(3):876– 91. DOI: 10.1007/s12072-024-10644-y

14. Bou Jaoude J., Al Bacha R., Abboud B. Will artificial intelligence reach any limit in gastroenterology? Artif Intell Gastroenterol. 2024;5(2):91336. DOI: 10.35712/aig.v5.i2.91336

15. Ministry of Health of the Russian Federation. Clinical guidelines. Senile asthenia. 2021. (In Russ.).

16. Khalafyan A.A., Temerdashev Z.A., Abakumov A.G. Influence of cluster data structure on neural network model predictive properties. Iskusstvenniy Intellekt i Prinyatie Resheniy. 2025;2:19–31. (In Russ.). DOI: 10.14357/20718594250202

17. Veerankutty F.H., Jayan G., Yadav M.K., Manoj K.S., Yadav A., Nair S.R., et al. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol. 2021;13(12):1977–90. DOI: 10.4254/wjh.v13.i12.1977

18. Zhou X.Q., Huang S., Shi X.M., Liu S., Zhang W., Shi L., et al. Global trends in artificial intelligence applications in liver disease over seventeen years. World J Hepatol. 2025;17(3):101721. DOI: 10.4254/wjh.v17.i3.101721

19. Zhang G., Li Y., Zhang X., Huang L., Cheng Y., Shen W. Identifying mild hepatic encephalopathy based on multilayer modular algorithm and machine learning. Front Neurosci. 2020;14:627062. DOI: 10.3389/fnins.2020.627062

20. Bhat M., Rabindranath M., Chara B.S., Simonetto D.A. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol. 2023;78(6):1216–33. DOI: 10.1016/j.jhep.2023.01.006

21. Tandon P., Montano-Loza A.J., Lai J.C., Dasarathy S., Merli M. Sarcopenia and frailty in decompensated cirrhosis. J Hepatol. 2021;75(Suppl 1):147–62. DOI: 10.1016/j.jhep.2021.01.025

22. Lai J.C., Tandon P., Bernal W., Tapper E.B., Ekong U., Dasarathy S., et al. Malnutrition, frailty, and sarcopenia in patients with cirrhosis: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2021;74(3):1611–44. DOI: 10.1002/hep.32049