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Related Concept Videos

Appendicitis-II: Diagnostic Studies and Management01:29

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Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis.

Ričards Marcinkevičs1, Patricia Reis Wolfertstetter2, Ugne Klimiene1

  • 1Department of Computer Science, ETH Zurich, Universitätstrasse 6, Zürich, 8092, Switzerland.

Medical Image Analysis
|November 24, 2023
PubMed
Summary

This study introduces interpretable machine learning models using ultrasound images to predict appendicitis in children. The models offer understandable insights for clinicians without sacrificing diagnostic accuracy.

Keywords:
ClassificationConceptsInterpretable machine learningMultiview learningPediatric appendicitisUltrasound imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pediatric Surgery

Background:

  • Appendicitis is a common cause of pediatric abdominal surgery.
  • Existing decision support systems often overlook abdominal ultrasound data.
  • Ultrasound is noninvasive and widely accessible for pediatric patients.

Purpose of the Study:

  • To develop interpretable machine learning models for appendicitis diagnosis, management, and severity prediction.
  • To leverage abdominal ultrasound images, which are frequently underutilized in current systems.
  • To create models that are understandable and interactive for clinicians.

Main Methods:

  • Utilized concept bottleneck models (CBMs) for interpretable predictions.
  • Extended CBMs to handle multi-view imaging and incomplete concept sets.
  • Trained models on a dataset of 579 pediatric patients with 1709 ultrasound images and clinical data.

Main Results:

  • The extended multi-view CBM achieved an AUROC of 0.80 and AUPR of 0.92 for diagnosis prediction.
  • The models provide human-understandable and intervenable predictions.
  • Performance was comparable to black-box neural networks on the same dataset.

Conclusions:

  • Interpretable machine learning models using ultrasound can effectively predict appendicitis in children.
  • The proposed CBM approach offers a valuable, clinician-friendly tool for appendicitis assessment.
  • This method enhances decision support by integrating readily available ultrasound data.