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Related Concept Videos

Appendicitis-II: Diagnostic Studies and Management01:29

Appendicitis-II: Diagnostic Studies and Management

145
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...
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Appendicitis-I: Introduction01:22

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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...
614

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Machine Learning and Feature Selection in Pediatric Appendicitis.

John Kendall1, Gabriel Gaspar1, Derek Berger1

  • 1Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada.

Tomography (Ann Arbor, Mich.)
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts pediatric appendicitis diagnosis, management, and severity. Ultrasound features enhance diagnostic accuracy but are not crucial for predicting management or severity outcomes.

Keywords:
appendicitisclassificationmachine learningpediatricspredictive medicine

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Pediatric Surgery

Background:

  • Accurate prediction of pediatric appendicitis diagnosis, management, and severity is crucial for effective clinical decision-making.
  • Machine learning (ML) models offer potential for improving diagnostic accuracy and patient outcomes.
  • The role of ultrasound (US) image-descriptive features in ML model performance requires further investigation.

Purpose of the Study:

  • To evaluate the predictive performance of various ML models and feature selection techniques for pediatric appendicitis.
  • To assess the impact of US image-descriptive features on model performance and explainability.
  • To identify optimal ML approaches for predicting diagnosis, management, and severity in pediatric appendicitis.

Main Methods:

  • Retrospective cohort study of 781 pediatric patients (0-18 years) with appendicitis.
  • Development and validation of ML models including Random Forest, Logistic Regression, SGD, and LGBM.
  • Exhaustive pairing of ML models with filter-based, embedded, and wrapper feature selection methods, including a novel redundancy-aware approach.
  • Evaluation of models with and without US image-descriptive features using accuracy and AUROC metrics.

Main Results:

  • US features significantly improved diagnostic accuracy, reducing model bias.
  • Models achieved high performance: diagnosis (Random Forest with LGBM feature selection: 98.1% accuracy, 0.993 AUROC), management (Random Forest: 93.9% accuracy, 0.980 AUROC), and severity (LGBM with filter-based feature selection: 90.1% accuracy, 0.931 AUROC).
  • US features were not essential for maximizing accuracy in predicting management or severity.

Conclusions:

  • High-performing, interpretable ML models can effectively predict key clinical outcomes in pediatric appendicitis.
  • US image features enhance diagnostic accuracy but are not critical for predicting management or severity.
  • ML offers a promising tool for optimizing clinical decision-making in pediatric appendicitis.