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Konstantia Zarkogianni1,2, Edmund Dervakos3, George Filandrianos3

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This study introduces the smarty4covid dataset for detecting respiratory conditions using Artificial Intelligence (AI) and m-health. A new framework provides counterfactual explanations for AI models detecting COVID-19 risk.

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Area of Science:

  • Respiratory health
  • Artificial Intelligence
  • m-Health

Background:

  • The COVID-19 pandemic heightened interest in AI and m-Health for detecting respiratory conditions.
  • Existing research focuses on identifying new biomarkers for respiratory abnormalities.
  • Crowd-sourced data collection presents opportunities and challenges for respiratory health monitoring.

Purpose of the Study:

  • To introduce the smarty4covid dataset, a comprehensive resource for developing AI-based COVID-19 risk detection models.
  • To develop and validate a novel framework for generating counterfactual explanations in opaque AI models for COVID-19 risk detection.
  • To leverage AI and m-Health for early detection and monitoring of respiratory conditions.

Main Methods:

  • Development of the smarty4covid dataset, including audio signals (cough, breathing, voice) and self-reported information, using a crowd-sourcing approach.
  • Creation of a Web Ontology Language (OWL) knowledge base for data consolidation, complex queries, and reasoning.
  • Utilization of the OWL knowledge base to develop models for extracting respiratory indicators and segmenting audio recordings.
  • Proposal and validation of a new framework for generating counterfactual explanations in AI-based COVID-19 risk detection models.

Main Results:

  • The smarty4covid dataset provides a rich resource with over 17,000 audio recordings and associated metadata.
  • Models developed using the dataset can extract clinically informative respiratory indicators from breathing sounds.
  • The proposed framework successfully generates counterfactual explanations for opaque AI models, enhancing interpretability.
  • The OWL knowledge base facilitates data integration and advanced analytical capabilities.

Conclusions:

  • The smarty4covid dataset and associated framework offer a significant advancement in AI-driven respiratory health monitoring.
  • Interpretable AI models are crucial for reliable COVID-19 risk detection and clinical decision-making.
  • The integration of m-Health and AI holds substantial promise for public health surveillance and personalized medicine.
  • Further research can expand the dataset and framework to encompass a wider range of respiratory conditions.