Related Experiment Video

Updated: Aug 28, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

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

Anas Shikha1, Asem Kasem2

  • 1Department of Pediatric Surgery, Raja Isteri Pengiran Anak Saleha (RIPAS) Hospital, Jalan Putera Al-Muhtadee Billah, Bandar Seri Begawan, Brunei.

European Journal of Pediatric Surgery : Official Journal of Austrian Association of Pediatric Surgery ... [Et Al] = Zeitschrift Fur Kinderchirurgie
|September 16, 2022
PubMed

Insights

Diagnosing appendicitis in children is challenging. An AI model, AiPAD, accurately identifies appendicitis using clinical data alone, avoiding imaging and improving patient outcomes.

Area of Science:

  • Pediatric Medicine
  • Artificial Intelligence in Healthcare
  • Diagnostic Accuracy

Background:

  • Diagnosing appendicitis in young children (0-12 years) presents significant challenges, even with advanced imaging techniques.
  • Existing diagnostic scoring models show variable accuracy upon validation in pediatric populations.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) diagnostic model for appendicitis in children using only clinical and laboratory parameters.
  • To prospectively validate the AI model's diagnostic performance without relying on imaging data.

Main Methods:

  • Stage I involved developing the AI Pediatric Appendicitis Decision-tree (AiPAD) model using observational data from 166 children (0-12 years) with acute appendicitis.
  • 10-fold cross-validation (XV) was employed for initial model evaluation, simulating prospective validation.
  • Stage II involved prospective validation of the AiPAD model on 139 children, comparing results with XV estimates.

Main Results:

  • The developed AiPAD model demonstrated high accuracy with XV estimation: 93.5% ± 5.8% (91.4% PPV, 94.8% NPV).
  • Prospective validation confirmed the model's accuracy, achieving 97.1% overall accuracy (96.7% PPV, 97.4% NPV).
  • The AI model is both accurate and explainable, providing reliable diagnostic predictions.

Conclusions:

  • The validated AiPAD model offers a highly accurate, easy-to-understand tool for diagnosing pediatric appendicitis without imaging.
  • This AI-driven approach can lead to significant practical benefits, including improved patient outcomes and reduced healthcare costs.
  • AiPAD provides an invaluable non-imaging tool for pediatric appendicitis diagnosis.
Abstract

More Related Videos

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.2K
A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

3.9K

Related Concept Videos

Appendicitis-II: Diagnostic Studies and Management01:29

Appendicitis-II: Diagnostic Studies and Management

Diagnosing and managing appendicitis requires a structured and comprehensive approach that spans from initial assessment to postoperative care. Here is an overview of the process:
Diagnosing Appendicitis
It requires a multifaceted approach, starting with a detailed physical examination to pinpoint the location and nature of the pain and identify any associated symptoms. Laboratory tests play a crucial role. A complete Blood Count (CBC) typically reveals leukocytosis (an increased number of...
132
Appendicitis-I: Introduction01:22

Appendicitis-I: Introduction

The appendix, a small, narrow, blind tube extending from the inferior part of the cecum, is widely regarded as a vestigial organ, having lost much of its original function through evolution. Despite its diminished role, the appendix can become inflamed, a condition known as appendicitis.
Etiology: Appendicitis can arise from various causes, primarily rooted in the obstruction of the appendix lumen. Factors contributing to this obstruction include fecal accumulation, lymphoid hyperplasia and, in...
464
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
138