ARTIFICIAL INTELLIGENCE IN GASTROINTESTINAL SURGERY

Artificial_Intelligence_in_Gastrointestinalkapak

ARTIFICIAL INTELLIGENCE (AI) IN THE MANAGEMENT OF PANCREATIC AND SPLENIC DISEASES

Mehmet Akif Türkoğlu

Gazi University Faculty of Medicine, Department of General Surgery, Ankara, Türkiye

Türkoğlu MA. Artificial Intelligence (AI) in the Management of Pancreatic and Splenic Diseases. Çaycı HM, ed. Artificial Intelligence (AI) in Gastrointestinal Surgery. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.101-111.

ABSTRACT

This comprehensive chapter explores the current applications and future potential of artificial intelligence (AI) in managing pancreatic and splenic diseases. In pancreatic diseases, AI has shown promising results in various aspects: predicting acute pancreatitis severity and complications with higher accuracy than traditional scoring systems; improving the diagnosis and staging of pancreatic ductal adenocarcinoma (PDAC) through enhanced imaging analysis; predicting postoperative outcomes and survival in pancreatic cancer patients; and risk-stratifying pancreatic cystic lesions. For pancreatic neuroendocrine tumors (PNETs), AI-powered radiomic analysis has demonstrated effectiveness in predicting tumor grades and potential outcomes. In splenic diseases, AI applications have primarily focused on automated spleen segmentation for accurate volume measurement in splenomegaly and improving the diagnosis of splenic trauma through ultrasound image analysis. Deep learning models, particularly convolutional neural networks (CNNs), have shown superior performance compared to traditional diagnostic methods across various applications. While these AI applications show great promise, most still require external validation with larger datasets before widespread clinical implementation. The integration of AI into clinical practice could significantly enhance diagnostic accuracy, improve risk stratification, and ultimately lead to better patient outcomes in pancreatic and splenic diseases.

Keywords: Artificial intelligence; Clinical decision support; Radiomics; Deep learning; Pancreatic diseases; Splenic diseases

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