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Related Experiment Videos

Robust and Interpretable AI for Acute Appendicitis: A Simulation-to-Clinical Validation Pipeline.

Kazi Nur Uddin1, Ebrima Njie1, Ruoming Jin2

  • 1Department of Mathematical Sciences, Kent State University, Kent, OH, 44242, United States.

Computer Methods and Programs in Biomedicine
|June 4, 2026
PubMed
Summary

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...
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...

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This summary is machine-generated.

The Robust and Interpretable Simulation-Analysis (RISA) framework enhances diagnostic AI for acute appendicitis by integrating simulation and clinical validation. This approach ensures reliable and interpretable machine learning models for accurate medical diagnosis.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Diagnostic Accuracy

Background:

  • Accurate diagnosis of acute appendicitis is challenging due to overlapping symptoms and varied presentations.
  • Existing diagnostic methods require improvement for reliability and generalizability.
  • The need for robust and interpretable AI tools in emergency medicine is critical.

Purpose of the Study:

  • To introduce and evaluate the Robust and Interpretable Simulation-Analysis (RISA) framework for assessing diagnostic classifiers.
  • To benchmark the performance, robustness, and generalizability of various machine learning algorithms.
  • To ensure the interpretability and clinical validity of AI models for acute appendicitis diagnosis.

Main Methods:

  • Six supervised learning algorithms were evaluated across 24 simulation scenarios with varying data characteristics.
Keywords:
Acute AppendicitisClassifier PerformanceDiagnostic Decision SupportExplainable AI (XAI)Machine Learning (ML)Simulation-to-Clinical Validation

Related Experiment Videos

  • Clinical validation was performed on two independent datasets: adult Appendicitis (n=106) and Regensburg Pediatric Appendicitis (n=782).
  • Interpretability was assessed using Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP).

    • Main Results:

    • Linear Discriminant Analysis and Support Vector Machine showed balanced performance in simulations; Random Forest excelled in nonlinear scenarios.
    • Linear Discriminant Analysis achieved the highest AUC (0.908) on the appendicitis dataset.
    • Support Vector Machine demonstrated the highest discrimination (AUC=0.762) on the RPA dataset, with LIME/SHAP confirming clinically relevant biomarkers.

    Conclusions:

    • The RISA framework provides a reproducible method for developing reliable diagnostic AI.
    • It bridges algorithmic robustness with clinical reliability for acute appendicitis diagnosis.
    • The study facilitates the implementation of explainable AI in clinical practice.