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Liu et al. Art Int Surg. 2025;5:425-33 https://dx.doi.org/10.20517/ais.2025.01 Page 429

metrics such as abdominopelvic VAT volume, abdominopelvic inner abdominal musculature (IAM)
volume, and the pelvic VAT to outer abdominal musculature (OAM) volume ratio - as key contributors to
the pathophysiology of incisional hernia. The authors propose future clinical translation of these findings
into a point-of-care tool for preoperative hernia risk assessment.

Preoperative planning
Only one study has evaluated the use of AI in preoperative planning for complex abdominal wall
reconstruction. The authors utilized abdominal CT scans taken both at rest and during Valsalva maneuvers
to generate models of tissue elasticity, mesh placement, and fixation point distribution. They then applied
algorithms based on non-rigid B-spline registration combined with AI to integrate these results for surgical
planning. This preoperative planning model was applied to 96 patients, resulting in reduced postoperative
pain and no recurrences after one year . The authors also described their calculation of a gained resistance
[22]
to impacts related to pressure (GRIP) value, considering mesh type, fixation method, positioning, overlap,
native tissue elasticity, and peritoneal closure.

[23]
Non-rigid B-spline is a statistical deformation model commonly used in medical image processing . It
allows flexible matching of two images and has been incorporated into radiation oncology for image
matching during respiratory motion . Evidently, non-rigid B-splines have supported clinical practice with
[24]
excellent outcomes in several hundred cases, demonstrating favorable three-year results.

Elhage et al. used deep learning models (DLMs) to analyze preoperative imaging with the goal of predicting
[25]
operative and postoperative outcomes . They assessed the ability of DLMs to predict the need for
advanced surgical techniques, such as component separation. When evaluating CT images, the DLMs
outperformed expert surgeons in predicting surgical complexity and the necessity for component
separation, achieving an accuracy of 81.3%, sensitivity of 88.9%, and specificity of 73.5%. In comparison, the
surgeon group demonstrated 65% accuracy, 53.3% sensitivity, and 76.7% specificity. The authors conclude
that further refinement of this technology can improve preoperative planning and enhance the informed
consent process in surgical care.

Predicting postoperative complications
Licari et al. investigated the use of a kernel support vector machine (KSVM)-based intelligent data analysis
technique to identify and validate known risk factors for hernia recurrence . Their machine learning
[26]
approach focused on analyzing and mining data to confirm established risk factors such as age, sex, urgency
of surgery, surgical technique, ASA class, BMI, comorbidities, mesh type and size, and postoperative
infections. Their relapse classification model achieved an accuracy of 86.67%, sensitivity of 86.25%, and
specificity of 87.14%.

In addition to predicting surgical complexity using DLMs, Elhage et al. developed models to predict surgical
site infection and pulmonary complications. The model for surgical site infection achieved 85.2% accuracy,
78.5% sensitivity, and 87.4% specificity. However, their model for pulmonary complications was less
effective, with 77.5% accuracy, 26.9% sensitivity, and 84.0% specificity. Notably, they used conventional
[25]
8-layer convolutional neural networks .

Ayuso et al. also assessed hernia recurrence using CT imaging-based DLMs . They compared a
[27]
conventional DLM trained on both positive and negative outcomes with models based on a generative
adversarial network anomaly (GANomaly) framework, which is trained only on negative outcomes and
detects positive cases as anomalies. Their findings showed that the conventional method had higher