Artificial intelligence use in abdominal wall reconstruction: a systematic review

Abstract

Aim: The use of artificial intelligence (AI) in medicine has grown significantly in recent years. This systematic review aims to highlight current trends in the application of AI specifically in abdominal wall reconstruction, which represents one of many medical fields utilizing AI technology.

Methods: A systematic review was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Electronic databases including PubMed, Google Scholar, EBSCO, Ovid, and the Cochrane Library were searched for studies published between 2000 and 2024 that evaluated AI applications in abdominal wall reconstruction.

Results: A total of 142 publications were identified, of which 12 met the inclusion criteria and were included in this review. All included studies were published between 2019 and 2024. Among these, 2 studies investigated AI models for predicting hernia occurrence and the need for abdominal wall reconstruction; 1 study focused on AI for preoperative planning; 6 articles examined AI-based prediction of postoperative complications; and 3 publications explored the use of AI to answer patient questions.

Conclusion: Current research on AI in abdominal wall reconstruction primarily focuses on predicting postoperative outcomes and minimizing complications. However, there is no established consensus regarding the optimal applications or methodologies for integrating AI in this surgical field.

Keywords

Artificial intelligence / AI use in surgery / abdominal wall reconstruction

References

[1] Goodenough CJ,Kao LS.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 Surg2015;220:405-13 PMCID:PMC4372474

[2] Marxen T,Losken A.Evaluating the effect of socioeconomic status on complex abdominal wall reconstruction outcomes.Ann Plast Surg2022;89:670-4

[3] Poulose BK,Phillips S.Epidemiology and cost of ventral hernia repair: making the case for hernia research.Hernia2012;16:179-83

[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 Surg2012;204:144-50

[5] Sharma I,Kaushik R.Application of AI in everyday life.Ind Eng J2022;51:33-8

[6] Yordanova MZ.The applications of artificial intelligence in radiology: opportunities and challenges.Eur J Med Health Sci2024;6:11-4

[7] Buijs E,Mazziotta F,Carrafiello G.Clinical impact of AI in radiology department management: a systematic review.Radiol Med2024;129:1656-66 PMCID:PMC11554795

[8] Singh M,Khanna NN.Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.EClinicalMedicine2024;73:102660 PMCID:PMC11154124

[9] Panjiyar BK,Nashat H.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?.Cureus2023;15:e43003 PMCID:PMC10478604

[10] Qin F.Artificial intelligence in plastic surgery: current developments and future perspectives.Plast Aesthet Res2023;10:3

[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,Rostami B,Niezgoda J.Image-based artificial intelligence in wound assessment: a systematic review.Adv Wound Care2022;11:687-709

[13] Huang RW,Hsieh YH.Reliability of postoperative free flap monitoring with a novel prediction model based on supervised machine learning.Plast Reconstr Surg2023;152:943e-52e

[14] Popa SL,Brata VD.Artificial intelligence and medical specialties: support or substitution?.Med Pharm Rep2024;97:409-18 PMCID:PMC11534378

[15] Haug CJ.Artificial intelligence and machine learning in clinical medicine, 2023.N Engl J Med2023;388:1201-8

[16] PRISMA Statement. Available from: https://www.prisma-statement.org/. [Last accessed on 12 Aug 2025]

[17] Talwar AA,McAuliffe PB.Optimal computed tomography-based biomarkers for prediction of incisional hernia formation.Hernia2024;28:17-24 PMCID:PMC11235401

[18] Opitz D.Popular ensemble methods: an empirical study.J Artif Intell Res1999;11:169-98Available from: https://www.d.umn.edu/~rmaclin/publications/opitz-jair99.pdf. [Last accessed on 12 Aug 2025]

[19] Breiman L.Random forests.Mach Learn2001;45:5-32

[20] Cortes C.Support-vector networks.Mach Learn1995;20:273-97

[21] McAuliffe PB,Talwar AA.Preoperative computed tomography morphological features indicative of incisional hernia formation after abdominal surgery.Ann Surg2022;276:616-25 PMCID:PMC9484790

[22] Kallinowski F,Löffler T.Biomechanics applied to incisional hernia repair - considering the critical and the gained resistance towards impacts related to pressure.Clin Biomech2021;82:105253

[23] Du X,Wang Y,Lei T.A parallel nonrigid registration algorithm based on B-spline for medical images.Comput Math Methods Med2016;2016:7419307 PMCID:PMC5174751

[24] Chun SY.A simple regularizer for B-spline nonrigid image registration that encourages local invertibility.IEEE J Sel Top Signal Process2009;3:159-69 PMCID:PMC3004236

[25] Elhage SA,Ayuso SA.Development and validation of image-based deep learning models to predict surgical complexity and complications in abdominal wall reconstruction.JAMA Surg2021;156:933-40 PMCID:PMC8264757

[26] Licari L,Campanella S.Use of the KSVM-based system for the definition, validation and identification of the incisional hernia recurrence risk factors.G Chir2019;40:32-8

[27] Ayuso SA,Zhang Y.Predicting rare outcomes in abdominal wall reconstruction using image-based deep learning models.Surgery2023;173:748-55

[28] O’Brien WJ,Gupta K.Neural network model to detect long-term skin and soft tissue infection after hernia repair.Surg Infect2021;22:668-74

[29] Hassan AM,Asaad M.Novel machine learning approach for the prediction of hernia recurrence, surgical complication, and 30-day readmission after abdominal wall reconstruction.J Am Coll Surg2022;234:918-27

[30] Wilson HH,Ku D.Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up.Surg Endosc2024;38:3984-91 PMCID:PMC11219459

[31] Berger RL,Hicks SC,Kao LS.Development and validation of a risk-stratification score for surgical site occurrence and surgical site infection after open ventral hernia repair.J Am Coll Surg2013;217:974-82

[32] Nelson JA,Chung CC.Readmission following ventral hernia repair: a model derived from the ACS-NSQIP datasets.Hernia2015;19:125-33

[33] Fischer JP,Tuggle CT,Kovach SJ.Wound risk assessment in ventral hernia repair: generation and internal validation of a risk stratification system using the ACS-NSQIP.Hernia2015;19:103-11

[34] Chen JH.Machine learning and prediction in medicine - beyond the peak of inflated expectations.N Engl J Med2017;376:2507-9 PMCID:PMC5953825

[35] Li W,Chen F,Yu H.Exploring the potential of ChatGPT-4 in responding to common questions about abdominoplasty: an AI-based case study of a plastic surgery consultation.Aesthetic Plast Surg2024;48:1571-83

[36] Lim B,Cuomo R.Can AI answer my questions? Utilizing artificial intelligence in the perioperative assessment for abdominoplasty patients.Aesthetic Plast Surg2024;48:4712-24 PMCID:PMC11645314

[37] Lima DL,Liu J.How appropriate are recommendations of online chat-based artificial intelligence (ChatGPT) to common questions on ventral hernia repair?.J Laparoendosc Adv Surg Tech A2024;34:365-7

[38] Stewart JE,Dwivedi G.Medical specialties involved in artificial intelligence research: is there a leader.Tasman Med J2020;2:20-7Available from: https://tasmanmedicaljournal.com/2020/02/medical-specialties-involved-in-artificial-intelligence-research-is-there-a-leader/. [Last accessed on 12 Aug 2025]

[39] Van Den Hoven P,Nerup N.Quantitative perfusion assessment using indocyanine green during surgery - current applications and recommendations for future use.Langenbecks Arch Surg2023;408:67 PMCID:PMC9879827