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Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis

Coralie Geric1, Gamuchirai Tavaziva2, Marianne Breuninger3

  • 1McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Canada; Department of Medicine, McGill University, Montreal, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.

International Journal of Infectious Diseases : IJID : Official Publication of the International Society for Infectious Diseases
|September 5, 2024
PubMed
Summary
This summary is machine-generated.

Multivariable models using computer-aided detection (CAD) scores for chest X-ray (CXR) classification significantly improved tuberculosis (TB) diagnosis specificity compared to standard threshold methods. This approach enhances diagnostic accuracy for TB detection.

Keywords:
Chest radiographyDeep learningPrediction modellingTuberculosis

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Area of Science:

  • Medical Imaging
  • Infectious Disease Diagnostics
  • Biostatistics

Background:

  • Computer-aided detection (CAD) software quantifies tuberculosis (TB)-compatible chest X-ray (CXR) abnormalities as continuous scores.
  • Current practice involves selecting a threshold value for binary CXR classification, which may limit diagnostic accuracy.

Purpose of the Study:

  • To assess the diagnostic accuracy of an alternative approach using CAD scores in multivariable modeling for TB triage.
  • To compare the performance of multivariable models with the current threshold-based CAD application.

Main Methods:

  • Pooled individual patient data from four studies.
  • Used logistic regression to model microbiologically confirmed TB, incorporating CAD scores, study site, age, sex, HIV status, and prior TB.
  • Compared specificity at target sensitivities (≥90%) between multivariable models and threshold-based CAD approaches.

Main Results:

  • Multivariable models demonstrated excellent performance (AUCs: software A, 0.91; software B, 0.92).
  • Multivariable models increased specificity compared to threshold scores at 90% sensitivity (e.g., software A: 75% vs 71%; software B: 75% vs 69%).
  • Included 4,733 participants; 17% had TB.

Conclusions:

  • Incorporating CAD scores into multivariable models significantly outperformed the current practice of threshold-based CXR classification for TB diagnosis.
  • This refined approach offers improved specificity in TB detection using chest X-rays.