CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV
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 shortage...
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Nature Portfolio
2020
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oai:doaj.org-article:7e39bc9219eb4fc4b15d71d68221dc992021-12-02T17:41:16ZCheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV10.1038/s41746-020-00322-22398-6352https://doaj.org/article/7e39bc9219eb4fc4b15d71d68221dc992020-09-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00322-2https://doaj.org/toc/2398-6352Abstract 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.Pranav RajpurkarChloe O’ConnellAmit SchechterNishit AsnaniJason LiAmirhossein KianiRobyn L. BallMarc MendelsonGary MaartensDaniël J. van HovingRulan GrieselAndrew Y. NgTom H. BoylesMatthew P. LungrenNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020) |
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Computer applications to medicine. Medical informatics R858-859.7 |
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Computer applications to medicine. Medical informatics R858-859.7 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 CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV |
description |
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. |
format |
article |
author |
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 |
author_facet |
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 |
author_sort |
Pranav Rajpurkar |
title |
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV |
title_short |
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV |
title_full |
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV |
title_fullStr |
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV |
title_full_unstemmed |
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV |
title_sort |
chexaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with hiv |
publisher |
Nature Portfolio |
publishDate |
2020 |
url |
https://doaj.org/article/7e39bc9219eb4fc4b15d71d68221dc99 |
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