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Review
. 2024 Sep 6:3:13059.
doi: 10.3389/jaws.2024.13059. eCollection 2024.

Artificial Intelligence-What to Expect From Machine Learning and Deep Learning in Hernia Surgery

Robert Vogel  1 , Björn Mück  1
Affiliations
Review

Artificial Intelligence-What to Expect From Machine Learning and Deep Learning in Hernia Surgery

Robert Vogel et al. J Abdom Wall Surg. .

Abstract

This mini-review explores the integration of Artificial Intelligence (AI) within hernia surgery, highlighting the role of Machine Learning (ML) and Deep Learning (DL). The term AI incorporates various technologies including ML, Neural Networks (NN), and DL. Classical ML algorithms depend on structured, labeled data for predictions, requiring significant human oversight. In contrast, DL, a subset of ML, generally leverages unlabeled, raw data such as images and videos to autonomously identify patterns and make intricate deductions. This process is enabled by neural networks used in DL, where hidden layers between the input and output capture complex data patterns. These layers' configuration and weighting are pivotal in developing effective models for various applications, such as image and speech recognition, natural language processing, and more specifically, surgical procedures and outcomes in hernia surgery. Significant advancements have been achieved with DL models in surgical settings, particularly in predicting the complexity of abdominal wall reconstruction (AWR) and other postoperative outcomes, which are elaborated in detail within the context of this mini-review. The review method involved analyzing relevant literature from databases such as PubMed and Google Scholar, focusing on studies related to preoperative planning, intraoperative techniques, and postoperative management within hernia surgery. Only recent, peer-reviewed publications in English that directly relate to the topic were included, highlighting the latest advancements in the field to depict potential benefits and current limitations of AI technologies in hernia surgery, advocating for further research and application in this evolving field.

Keywords: AI; deep learning; hernia surgery; machine learning; neural network.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Schematic depicting the relationship between AI, artificial intelligence; ML, machine learning; DL, deep learning; NN, neural networks.
FIGURE 2
FIGURE 2
Schematic visualization of a deep learning model with input layer, n + 1 hidden layers and output layer).
See this image and copyright information in PMC

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