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Explainability of radiomics through formal methods.

Giulia Varriano1, Pasquale Guerriero1, Antonella Santone1

  • 1Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.

Computer Methods and Programs in Biomedicine
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Summary

Formal methods enhance Artificial Intelligence explainability in radiomics for Coronavirus disease diagnosis. This approach improves understanding of AI predictions, aiding both patients and medical specialists.

Keywords:
Artificial intelligenceCOVID-19ExplainabilityFormal methodsModel checkingRadiomics

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Area of Science:

  • Radiomics
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • Artificial Intelligence (AI) is effective in radiomics but lacks transparency in prediction generation.
  • Explainability of AI results is crucial for patient understanding and clinical decision support.
  • Current AI models present challenges for medical practitioners in trusting and utilizing their outputs.

Purpose of the Study:

  • To address transparency issues in AI for radiomics using Formal methods.
  • To analyze the diagnostic performance of Formal methods in Coronavirus disease detection.
  • To connect radiomic features with clinical and radiological evidence for improved interpretability.

Main Methods:

  • Application of Formal methods, employing mathematical logic for automated diagnosis.
  • Analysis of radiomic features for Coronavirus disease diagnosis.
  • Investigating multi-slice approaches for feature localization and selection.

Main Results:

  • Formal methods enable statistical analysis of feature distributions and pattern recognition in disease models.
  • The approach allows for generalization of disease models and achieves high performance in results and interpretation.
  • Localization and selection of key image slices enhance model explainability.

Conclusions:

  • Clinical significance of First order radiomic features like Skewness and Kurtosis is confirmed.
  • The Minimum feature is suggested for exclusion due to its association with Computed Tomography (CT) lung imaging.
  • Formal methods offer a pathway to more interpretable and reliable AI-driven medical diagnoses.