Retrospective ct-analysis using machine learning and feature selection for optimizing preoperative preparation of patients with large and giant postoperative ventral hernias

Introduction. Purpose: this study aimed to develop a machine learning-based approach for identifying key CT abdominometry features to assist in the preoperative planning of patients with large and giant postoperative ventral hernias.

Materials and methods of research. A retrospective analysis was conducted on 90 patients examined between 2020 and 2024. Patients were divided into three groups: those without postoperative ventral hernias (n=30), patients with W2 hernias (n=30), and patients with large or giant hernias who underwent preoperative botulinum therapy (n=30). CT scans of the abdomen, retroperitoneum, and pelvis were performed, and parameters such as anterior abdominal wall muscle characteristics, abdominal circumference, hernia gate width, defect ratio, and component separation index were measured. Statistical analyses included ANOVA, the Kruskal-Wallis test, Student’s t-test, and the Mann-Whitney U test. Feature selection was performed using a random forest algorithm, logistic regression was utilized for predicting the need for botulinum therapy, and clustering methods (K-Means, DBSCAN) were applied to identify distinct patient subgroups.

Treatment results. Logistic regression achieved an accuracy of 86 % (AUC-ROC = 0,95). 'The defect ratio, component separation index, and right muscle area were identified as significant predictors for botulinum therapy. K-Means clustering delineated a subgroup in which 83 % of patients underwent botulinum therapy, underscoring the objective differentiation based on CT features. An optimal predictive threshold of 0.6 was established to minimize false negatives.

Conclusion. The application of machine learning techniques facilitates objective and personalized preoperative planning, thereby enhancing surgical decision-making in the management of postoperative ventral hernias.

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