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.
Keywords
AI - clinical decision - diagnostic model - pediatric appendicitis - AI decision tree
- AiPAD
