ARTIFICIAL INTELLIGENCE–BASED PREDICTIVE MODELING FOR SURGICAL PLANNING IN DECOMPENSATED COLOSTASIS

Egamov Yu.S., Latipov R.J.

Authors

Egamov Yu.S., Latipov R.J. Department of Surgical Diseases and Civil Protection

Keywords:

artificial intelligence, predictive modeling, colon resection, decompensated colostasis, surgical planning, machine learning.

Abstract

Decompensated colostasis is a life-threatening condition characterized by severe intestinal obstruction, ischemic changes, and risk of perforation. Determining the appropriate extent of colon resection remains a major surgical challenge. Artificial intelligence (AI) has emerged as a powerful tool for improving preoperative planning through predictive modeling and large-scale clinical data analysis. This study explores the application of AI-based systems in predicting tissue viability, defining resection margins, and estimating postoperative complication risks in patients with decompensated colostasis. The integration of AI into surgical decision-making may enhance operative precision, reduce unnecessary resections, and improve patient outcomes.

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