npj Digital Medicine (Sep 2020)

CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV

  • Pranav Rajpurkar,
  • Chloe O’Connell,
  • Amit Schechter,
  • Nishit Asnani,
  • Jason Li,
  • Amirhossein Kiani,
  • Robyn L. Ball,
  • Marc Mendelson,
  • Gary Maartens,
  • Daniël J. van Hoving,
  • Rulan Griesel,
  • Andrew Y. Ng,
  • Tom H. Boyles,
  • Matthew P. Lungren

DOI
https://doi.org/10.1038/s41746-020-00322-2
Journal volume & issue
Vol. 3, no. 1
pp. 1 – 8

Abstract

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Abstract Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (p = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower (p < 0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise.