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Page 432                                                               Liu et al. Art Int Surg. 2025;5:425-33  https://dx.doi.org/10.20517/ais.2025.01

               Conflicts of interest
               All authors declared that there are no conflicts of interest.


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               Copyright
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               REFERENCES
               1.       Goodenough CJ, Ko TC, Kao LS, et al. Development and validation of a risk stratification score for ventral incisional hernia after
                   abdominal surgery: hernia expectation rates in intra-abdominal surgery (the HERNIA Project). J Am Coll Surg. 2015;220:405-13.  DOI
                   PubMed  PMC
               2.       Marxen T, Faulkner HR, Losken A. Evaluating the effect of socioeconomic status on complex abdominal wall reconstruction
                   outcomes. Ann Plast Surg. 2022;89:670-4.  DOI  PubMed
               3.       Poulose BK, Shelton J, Phillips S, et al. Epidemiology and cost of ventral hernia repair: making the case for hernia research. Hernia.
                   2012;16:179-83.  DOI  PubMed
               4.       van Ramshorst GH, Eker HH, Hop WC, Jeekel J, Lange JF. Impact of incisional hernia on health-related quality of life and body
                   image: a prospective cohort study. Am J Surg. 2012;204:144-50.  DOI  PubMed
               5.       Sharma I, Kumar P, Kaushik R. Application of AI in everyday life. Ind Eng J. 2022;51:33-8.  DOI
               6.       Yordanova MZ. The applications of artificial intelligence in radiology: opportunities and challenges. Eur J Med Health Sci. 2024;6:11-
                   4.  DOI
               7.       Buijs E, Maggioni E, Mazziotta F, Lega F, Carrafiello G. Clinical impact of AI in radiology department management: a systematic
                   review. Radiol Med. 2024;129:1656-66.  DOI  PubMed  PMC
               8.       Singh M, Kumar A, Khanna NN, et al. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a
                   scoping review. EClinicalMedicine. 2024;73:102660.  DOI  PubMed  PMC
               9.       Panjiyar BK, Davydov G, Nashat H, et al. A systematic review: do the use of machine learning, deep learning, and artificial
                   intelligence improve patient outcomes in acute myocardial ischemia compared to clinician-only approaches? Cureus. 2023;15:e43003.
                   DOI  PubMed  PMC
               10.      Qin F, Gu J. Artificial intelligence in plastic surgery: current developments and future perspectives. Plast Aesthet Res. 2023;10:3.  DOI
               11.      Catania LJ. Foundations of artificial intelligence in healthcare and bioscience: a user friendly guide for IT professionals, healthcare
                   providers, researchers, and clinicians. Academic Press; 2020. Available from: https://www.google.com/books/edition/Foundations_of_
                   Artificial_Intelligence_i/2Mr2DwAAQBAJ?hl=zh-CN&gbpv=0. [Last accessed on 12 Aug 2025].
               12.      Anisuzzaman DM, Wang C, Rostami B, Gopalakrishnan S, Niezgoda J, Yu Z. Image-based artificial intelligence in wound assessment:
                   a systematic review. Adv Wound Care. 2022;11:687-709.  DOI  PubMed
               13.      Huang RW, Tsai TY, Hsieh YH, et al. Reliability of postoperative free flap monitoring with a novel prediction model based on
                   supervised machine learning. Plast Reconstr Surg. 2023;152:943e-52e.  DOI  PubMed
               14.      Popa SL, Ismaiel A, Brata VD, et al. Artificial intelligence and medical specialties: support or substitution? Med Pharm Rep.
                   2024;97:409-18.  DOI  PubMed  PMC
               15.      Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388:1201-8.  DOI
                   PubMed
               16.      PRISMA Statement. Available from: https://www.prisma-statement.org/. [Last accessed on 12 Aug 2025].
               17.      Talwar AA, Desai AA, McAuliffe PB, et al. Optimal computed tomography-based biomarkers for prediction of incisional hernia
                   formation. Hernia. 2024;28:17-24.  DOI  PubMed  PMC
               18.      Opitz D, Maclin R. Popular ensemble methods: an empirical study. J Artif Intell Res. 1999;11:169-98. Available from: https://www.d.
                   umn.edu/~rmaclin/publications/opitz-jair99.pdf. [Last accessed on 12 Aug 2025]
               19.      Breiman L. Random forests. Mach Learn. 2001;45:5-32.  DOI
               20.      Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273-97.  DOI
               21.      McAuliffe PB, Desai AA, Talwar AA, et al. Preoperative computed tomography morphological features indicative of incisional hernia
                   formation after abdominal surgery. Ann Surg. 2022;276:616-25.  DOI  PubMed  PMC
               22.      Kallinowski F, Ludwig Y, Löffler T, et al. Biomechanics applied to incisional hernia repair - considering the critical and the gained
                   resistance towards impacts related to pressure. Clin Biomech. 2021;82:105253.  DOI  PubMed
               23.      Du X, Dang J, Wang Y, Wang S, Lei T. A parallel nonrigid registration algorithm based on B-spline for medical images. Comput Math
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