Abstract
Introduction
This systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.
Methods
The PRISMA guidelines were followed throughout this systematic review. The ROBINS—I and Rob 2 tools were used to perform qualitative assessment of all studies included in this review. Recommendations were then summarized for the following pre-defined key items: protocol, research question, search strategy, study eligibility, data extraction, study design, risk of bias, publication bias, and statistical analysis.
Results
A total of 13 articles were ultimately included for this review, describing the use of machine learning and deep learning for hernia surgery. All studies were published from 2020 to 2023. Articles varied regarding the population studied, type of machine learning or Deep Learning Model (DLM) used, and hernia type. Of the thirteen included studies, all included either inguinal, ventral, or incisional hernias. Four studies evaluated recognition of surgical steps during inguinal hernia repair videos. Two studies predicted outcomes using image-based DMLs. Seven studies developed and validated deep learning algorithms to predict outcomes and identify factors associated with postoperative complications.
Conclusion
The use of ML for abdominal wall reconstruction has been shown to be a promising tool for predicting outcomes and identifying factors that could lead to postoperative complications.
Similar content being viewed by others
References
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP (2020) Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 27(9(2)):14. https://doi.org/10.1167/tvst.9.2.14
Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM et al (2020) Artificial intelligence and surgical decision-making. JAMA Surg 155(2):148–158. https://doi.org/10.1001/jamasurg.2019.4917
James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning: with applications in R. Springer, New York
Delahanty RJ, Kaufman D, Jones SS (2018) Development and evaluation of an automated machine learning algorithm for in-hospital mortality risk adjustment among critical care patients. Crit Care Med 46(6):e481–e488. https://doi.org/10.1097/CCM.0000000000003011
Adhikari L, Ozrazgat-Baslanti T, Ruppert M, Madushani RWMA, Paliwal S, Hashemighouchani H et al (2019) Improved predictive models for acute kidney injury with IDEA: intraoperative data embedded analytics. PLoS ONE 14(4):e0214904. https://doi.org/10.1371/journal.pone.0214904
Hao Du, Ghassemi MM, Feng M (2016) The effects of deep network topology on mortality prediction. Annu Int Conf IEEE Eng Med Biol Soc. 2016:2602–2605. https://doi.org/10.1109/EMBC.2016.7591263
Choi E, Schuetz A, Stewart WF, Sun J (2017) Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc 24(2):361–370. https://doi.org/10.1093/jamia/ocw112
Ayuso SA, Elhage SA, Zhang Y, Aladegbami BG, Gersin KS, Fischer JP et al (2023) Predicting rare outcomes in abdominal wall reconstruction using image-based deep learning models. Surgery 173(3):748–755. https://doi.org/10.1016/j.surg.2022.06.048
Baig SJ, Priya P (2021) Management of ventral hernia in patients with BMI > 30 Kg/m2: outcomes based on an institutional algorithm. Hernia 25(3):689–699. https://doi.org/10.1007/s10029-020-02318-z
Choi JH, Janjua H, Cios K, Rogers MP, Read M, Docimo S et al (2023) Machine learning analysis of postlaparoscopy hernias and “I’m leaving you to close” strategy. J Surg Res 290:171–177. https://doi.org/10.1016/j.jss.2023.04.016
Cui P, Zhao S, Chen W (2021) Identification of the vas deferens in laparoscopic inguinal hernia repair surgery using the convolutional neural network. J Healthc Eng. https://doi.org/10.1155/2021/5578089
Elhage SA, Deerenberg EB, Ayuso SA, Murphy KJ, Shao JM, Kercher KW et al (2021) Development and validation of image-based deep learning models to predict surgical complexity and complications in abdominal wall reconstruction. JAMA Surg 156(10):933–940. https://doi.org/10.1001/jamasurg.2021.3012
Gao J, Zagadailov P, Merchant AM (2021) The use of artificial neural network to predict surgical outcomes after inguinal hernia repair. J Surg Res 259:372–378. https://doi.org/10.1016/j.jss.2020.09.021
Hassan AM, Lu SC, Asaad M, Liu J, Offodile AC, Sidey-Gibbons C et al (2022) Novel machine learning approach for the prediction of hernia recurrence, surgical complication, and 30-day readmission after abdominal wall reconstruction. J Am Coll Surg 234(5):918–927. https://doi.org/10.1097/XCS.0000000000000141
McAuliffe PB, Desai AA, Talwar AA, Broach RB, Hsu JY, Serletti JM et al (2022) Preoperative computed tomography morphological features indicative of incisional hernia formation after abdominal surgery. Ann Surg 276(4):616–625. https://doi.org/10.1097/SLA.0000000000005583
O’Brien WJ, Ramos RD, Gupta K, Itani KMF (2021) Neural network model to detect long-term skin and soft tissue infection after hernia repair. Surg Infect (Larchmt) 22(7):668–674. https://doi.org/10.1089/sur.2020.354
Takeuchi M, Collins T, Ndagijimana A, Kawakubo H, Kitagawa Y, Marescaux J et al (2022) Automatic surgical phase recognition in laparoscopic inguinal hernia repair with artificial intelligence. Hernia 26(6):1669–1678. https://doi.org/10.1007/s10029-022-02621-x
Yan YD, Yu Z, Ding LP, Zhou M, Zhang C, Pan MM et al (2023) Machine learning to dynamically predict in-hospital venous thromboembolism after inguinal hernia surgery: results from the CHAT-1 study. Clin Appl Thromb Hemost 29:10760296231171082. https://doi.org/10.1177/10760296231171082
Zang C, Turkcan MK, Narasimhan S, Cao Y, Yarali K, Xiang Z et al (2023) Surgical phase recognition in inguinal hernia repair-AI-based confirmatory baseline and exploration of competitive models. Bioengineering (Basel) 10(6):654. https://doi.org/10.3390/bioengineering10060654
Ortenzi M, Rapoport Ferman J, Antolin A, Bar O, Zohar M, Perry O et al (2023) A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP). Surg Endosc 37(11):8818–8828. https://doi.org/10.1007/s00464-023-10375-5
Ortega-Deballon P, Renard Y, de Launay J, Lafon T, Roset Q, Passot G (2023) Incidence, risk factors, and burden of incisional hernia repair after abdominal surgery in France: a nationwide study. Hernia 27(4):861–871. https://doi.org/10.1007/s10029-023-02825-9
Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med 6(7):e1000100. https://doi.org/10.1371/journal.pmed.1000100
Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M et al (2016) ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355:i4919. https://doi.org/10.1136/bmj.i4919
Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I et al (2019) RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 366:l4898. https://doi.org/10.1136/bmj.l4898
Matheny ME, Whicher D, Thadaney IS (2020) Artificial intelligence in health care: a report from the national academy of medicine. JAMA 323(6):509–510. https://doi.org/10.1001/jama.2019.21579
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005
Funding
There was no funding for this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Diego L Lima, Raquel Nogueira, Joao Kasakewitch and Diana Nguyen declare no conflict of interest. Leandro Totti Cavazzola disclosure consulting fees from BD. Flavio Malcher discloses consulting fees from BD, Intuitive, Integra, DeepBlue, Allergan & Medtronic, outside the submitted study. Todd Heniford discloses surgical research and education grants and speaking honoraria from WL Gore.
Ethical approval, Human and animal rights and Informed consent
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lima, D.L., Kasakewitch, J., Nguyen, D.Q. et al. Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review. Hernia (2024). https://doi.org/10.1007/s10029-024-03069-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10029-024-03069-x