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Elhage et al. Art Int Surg. 2025;5:247-53 https://dx.doi.org/10.20517/ais.2024.87 Page 249

synthetic mesh for the occurrence of a skin and soft tissue infection related to their hernia repair over a five-
year period. The model performed very well in discriminating between patients with and without infection.
The authors believe their model could aid in future outcomes research by decreasing administrative burden
in assessing infectious outcomes over extended periods of time, as well as possibly automating surveillance
programs for identifying patients with an infection .
[19]
In 2022, Hassan et al. published their work on using AI to predict complications following AWR. Utilizing a
dataset of 725 patients and nine supervised ML algorithms, they aimed to preoperatively predict hernia
recurrence, surgical site occurrences, and 30-day readmission following AWR. Their algorithms
demonstrated good discriminatory performance for predicting hernia recurrence and 30-day readmission
measured by area under the receiver operating curve (AUC), with AUCs of 0.71 and 0.75, respectively.
Additionally, their algorithms were able to identify many unique significant predictors for hernia
recurrence, surgical site occurrences, and 30-day readmission. This study was an early example of an AI
algorithm using preoperative clinical data to predict postoperative outcomes in AWR - a tool that the
authors conjecture could help in providing patient-specific risk assessment and preoperative counseling .
[20]
In 2023, two studies focused on using AI to identify risk factors for hernia development rather than
outcome prediction [21,22] . Ortega-Deballon et al. investigated the predictors of hernia formation. Utilizing a
large national database, they evaluated 710,074 patients who underwent abdominal surgery, looking for
predictors of hernia recurrence by analyzing the patients who subsequently underwent at least one
incisional hernia repair within five years. They utilized both a Cox multivariable analysis and a ML analysis.
The study identified several risk factors for the development of incisional hernia, including age, length of
hospital stay, laparoscopy vs. laparotomy, and intraabdominal surgical site. When focusing on the ML
evaluation, pancreatic operations and colorectal operations were the procedures with the highest risk of
incisional hernia repair within five years. Age, laparotomy, and obesity also proved to be important risk
factors, even in surgical sites with lower risks of eventual need for hernia repair .
[21]
Choi et al. similarly used ML database analysis to evaluate the risk of hernia occurrence after laparoscopic
cholecystectomy. Their main aim was to evaluate if there was an increased risk of post-laparoscopy
incisional hernia for index operations performed at a teaching hospital. Utilizing multiple different ML
algorithms, they evaluated 117,570 laparoscopic cholecystectomy cases and the risk factors that led to
subsequent hernia development. They found that surgery performed at a teaching hospital did not increase
the risk of post-laparoscopy hernia formation. However, ML did identify other patient- and hospital-
[22]
specific factors that impacted the risk of incisional hernia development both positively and negatively .

AI IMAGING ANALYSIS
While database analysis using AI, such as in the studies detailed above, can afford more efficient and timely
analysis of small to large datasets, imaging analysis can potentially open even more doors in AWR research.
Medical imaging is comprised of a wealth of data, nuances of which, such as slight differences in shades of
grey, can be imperceptible to the human eye. AI can analyze images not just at record speeds compared to
humans, but can also assess changes and differences in images that humans cannot appreciate [23,24] .

The first study utilizing AI imaging analysis in the hernia and AWR population was by Elhage et al. in 2021.
This also happens to be the first use of DL algorithms in the field of AWR research. The authors utilized DL
algorithms to analyze preoperative computed tomography (CT) scans of 369 patients, with the goals of
predicting the need for component separation (a proxy for surgical complexity) and predicting
postoperative surgical site infections and pulmonary failure. Using preoperative images alone, the surgical