Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review

D L Lima; J Kasakewitch; D Q Nguyen; R Nogueira; L T Cavazzola; B T Heniford; F Malcher

doi:10.1007/s10029-024-03069-x

Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review

Hernia. 2024 May 18. doi: 10.1007/s10029-024-03069-x. Online ahead of print.

Authors

D L Lima¹, J Kasakewitch², D Q Nguyen³, R Nogueira⁴, L T Cavazzola⁵, B T Heniford⁶, F Malcher⁷

Affiliations

¹ Department of Surgery, Montefiore Medical Center, New York, NY, USA. dilaurentino@gmail.com.
² Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
³ Albert Einstein, College of Medicine, New York, USA.
⁴ Department of Surgery, Montefiore Medical Center, New York, NY, USA.
⁵ Federal University of Rio Grande Do Sul, Porto Alegre, Brazil.
⁶ Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA.
⁷ Division of General Surgery, NYU Langone, New York, USA.

PMID: 38761300
DOI: 10.1007/s10029-024-03069-x

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.

Keywords: Hernia; Machine learning; Recurrence; Robotic surgery.