The Development and Validation of Artificial Intelligence Pediatric Appendicitis Decision-Tree for Children 0 to 12 Years Old

Abstract

Abstract Introduction Diagnosing appendicitis in young children (0–12 years) still poses a special difficulty despite the advent of radiological investigations. Few scoring models have evolved and been applied worldwide, but with significant fluctuations in accuracy upon validation. Aim To utilize artificial intelligence (AI) techniques to develop and validate a diagnostic model based on clinical and laboratory parameters only (without imaging), in addition to prospective validation to confirm the findings. Methods In Stage-I, observational data of children (0–12 years), referred for acute appendicitis (March 1, 2016–February 28, 2019, n = 166), was used for model development and evaluation using 10-fold cross-validation (XV) technique to simulate a prospective validation. In Stage-II, prospective validation of the model and the XV estimates were performed (March 1, 2019–November 30, 2021, n = 139). Results The developed model, AI Pediatric Appendicitis Decision-tree (AiPAD), is both accurate and explainable, with an XV estimation of average accuracy to be 93.5% ± 5.8 (91.4% positive predictive value [PPV] and 94.8% negative predictive value [NPV]). Prospective validation revealed that the model was indeed accurate and close to the XV evaluations, with an overall accuracy of 97.1% (96.7% PPV and 97.4% NPV). Conclusion The AiPAD is validated, highly accurate, easy to comprehend, and offers an invaluable tool to use in diagnosing appendicitis in children without the need for imaging. Ultimately, this would lead to significant practical benefits, improved outcomes, and reduced costs.