Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children

Megan Palmer1, James A Seddon1,2, Marieke M van der Zalm1

  • 1Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Demond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa.

Insights

Computer-aided detection (CAD) for paediatric tuberculosis shows promise. Fine-tuning an adult CAD system significantly improved its ability to detect tuberculosis on children's chest x-rays, enhancing diagnostic accuracy.

Area of Science:

  • Medical Imaging
  • Paediatric Infectious Diseases
  • Artificial Intelligence in Healthcare

Background:

  • Diagnostic tools for paediatric tuberculosis are limited, often relying on clinical algorithms and chest x-rays.
  • Computer-aided detection (CAD) systems have shown potential for tuberculosis detection in adult chest x-rays.

Purpose of the Study:

  • To evaluate and optimize the performance of an adult CAD system (CAD4TB) for identifying tuberculosis in paediatric chest x-rays.
  • To assess the impact of fine-tuning the CAD system using a paediatric dataset.

Main Methods:

  • Chest x-rays from 620 children under 13 with presumptive tuberculosis were evaluated.
  • An adult CAD system (CAD4TB) was applied to chest x-rays, with performance measured against expert radiological references.
  • The CAD system was fine-tuned using a training set of paediatric chest x-rays and its performance compared to the original model.

Main Results:

  • The original CAD4TB model had an area under the receiver operating characteristic curve (AUC) of 0.58.
  • After fine-tuning with paediatric data, the AUC improved significantly to 0.72 (p = 0.0016).

Conclusions:

  • Fine-tuning an adult CAD system significantly improves its performance for detecting tuberculosis in paediatric chest x-rays.
  • CAD shows potential as an additional diagnostic tool for paediatric tuberculosis.
  • Further research with larger, diverse datasets is recommended to evaluate CAD's role in paediatric tuberculosis diagnosis and treatment algorithms.

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