Artificial Intelligence Assisted Scoring System for Prognosis and Mortality Prediction in Acute Pancreatitis
Abstract
Background: In this study, we aimed to evaluate the prognostic value of the Pancreatitis Artificial Intelligence (PanAI) score, a new AI-based scoring system, in predicting disease severity and in-hospital mortality in patients with acute pancreatitis (AP).
Methods: The study included 76 patients admitted to the emergency department with a diagnosis of AP between 01.01.2023 - 01.01.2024. Clinical and laboratory data of the patients were analyzed retrospectively. PanAI score, Ranson score, 48th hour Ranson score and Balthazar Computed Tomography Severity Index (CTSI) scores were calculated and their relationships with disease severity and in-hospital mortality were evaluated. Model performance was compared by ROC analysis.
Results: The mean age of the patients included in the study was 61.95±17.40 years and 44.2% of the patients were classified in the severe AP group. In-hospital mortality rate was 13.2%. The PanAI score was more accurate than other scores in predicting severe AP and in-hospital mortality (AUC=0.911). In logistic regression analysis, PanAI score was found to be an independent predictor of severe AP and mortality (p<0.001).
Conclusion: The PanAI score stands out as a strong prognostic indicator in predicting disease severity and mortality risk in AP patients. Due to its higher accuracy compared to traditional scoring systems, it can be an important tool in clinical decision-making.
Keywords
Acute Pancreatitis, Mortality, Risk Classification, Severity Assessment, PanAI Score
Supporting Institution
This research received no external funding.
Ethical Statement
This study was approved by Ordu University Clinical Research Ethics Committee. Decision No: 2024/59.
Thanks
The PanAI scoring system presented in this study was developed with the support of the Claude 3.7 Sonnet artificial intelligence model, which was utilized for scoring algorithm generation. The authors also used
DeepL as a language assistance tool during the English editing process. We would like to thank Alesta Translation for their assistance in the language review of the manuscript. All authors have read and approved the final version of the article.
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