Abuladze Ivan (Candidate of medical science
Assistant of the Department of faculty surgery
Patrice Lumumba Peoples' Friendship University of Russia
)
Barkhudarov Alexandr Alexeevich (Candidate of medical science
Associate Professor of the Department of faculty surgery
Patrice Lumumba Peoples' Friendship University of Russia
)
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This review examines current approaches to using artificial intelligence for preoperative prediction of postoperative peritonitis. It also presents a pilot study on a synthetic cohort of 60 patients with acute appendicitis, where a CatBoost-based model achieved an AUC-ROC of 0.86 and identified key risk predictors: free intraperitoneal fluid on ultrasound, C-reactive protein level, hypoalbuminemia, and pain duration. Methodological limitations of existing models are discussed, and recommendations for clinical implementation are provided.
Keywords:artificial intelligence, machine learning, preoperative prediction, postoperative complications, peritonitis, gradient boosting
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Citation link:
Abuladze I. , Barkhudarov A. A. APPLICATION OF ARTIFICIAL INTELLIGENCE FOR PREOPERATIVE PREDICTION OF THE RISK OF DEVELOPING POSTOPERATIVE PERITONITIS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2025. -№12. -С. 179-184 DOI 10.37882/2223-2966.2025.12.01 |
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