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Area of Science:

  • Respiratory Medicine
  • Infectious Diseases
  • Artificial Intelligence in Healthcare

Background:

  • Cough classification shows promise for respiratory disease screening.
  • Concerns exist regarding biases and dataset quality in current models.
  • Tuberculosis (TB) diagnosis requires improved, accessible screening methods.

Purpose of the Study:

  • To investigate the diagnostic potential of cough features for pulmonary tuberculosis (TB).
  • To develop and evaluate a cough classifier for distinguishing TB from other respiratory illnesses.

Main Methods:

  • Collected passive and forced cough sounds from 149 pulmonary TB patients and 46 controls in Nairobi.
  • Trained a ResNet18 cough classifier using cough scalogram images.
  • Validated the model using fivefold cross-validation and analyzed performance based on bacterial load and lung cavities.

Main Results:

  • The cough classifier achieved a cross-validation sensitivity of 0.70 (±0.11 SD).
  • Model performance was significantly better in subjects with higher bacterial load (ROC-AUC: 0.87) and lung cavities (ROC-AUC: 0.89).
  • Passive cough features demonstrated ability to differentiate TB from non-TB subjects.

Conclusions:

  • Passive cough characteristics can effectively distinguish tuberculosis from other respiratory conditions.
  • Cough features are associated with TB's bacterial burden and disease severity.
  • Smartphone-based cough analysis offers a potential tool for TB screening and diagnosis.