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Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis

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  • Eric Engle
  • Andrei Gabrielian
  • Alyssa Long
  • Darrell E Hurt
  • Alex Rosenthal

Abstract

Availability of trained radiologists for fast processing of CXRs in regions burdened with tuberculosis always has been a challenge, affecting both timely diagnosis and patient monitoring. The paucity of annotated images of lungs of TB patients hampers attempts to apply data-oriented algorithms for research and clinical practices. The TB Portals Program database (TBPP, https://TBPortals.niaid.nih.gov) is a global collaboration curating a large collection of the most dangerous, hard-to-cure drug-resistant tuberculosis (DR-TB) patient cases. TBPP, with 1,179 (83%) DR-TB patient cases, is a unique collection that is well positioned as a testing ground for deep learning classifiers. As of January 2019, the TBPP database contains 1,538 CXRs, of which 346 (22.5%) are annotated by a radiologist and 104 (6.7%) by a pulmonologist–leaving 1,088 (70.7%) CXRs without annotations. The Qure.ai qXR artificial intelligence automated CXR interpretation tool, was blind-tested on the 346 radiologist-annotated CXRs from the TBPP database. Qure.ai qXR CXR predictions for cavity, nodule, pleural effusion, hilar lymphadenopathy was successfully matching human expert annotations. In addition, we tested the 12 Qure.ai classifiers to find whether they correlate with treatment success (information provided by treating physicians). Ten descriptors were found as significant: abnormal CXR (p = 0.0005), pleural effusion (p = 0.048), nodule (p = 0.0004), hilar lymphadenopathy (p = 0.0038), cavity (p = 0.0002), opacity (p = 0.0006), atelectasis (p = 0.0074), consolidation (p = 0.0004), indicator of TB disease (p =

Suggested Citation

  • Eric Engle & Andrei Gabrielian & Alyssa Long & Darrell E Hurt & Alex Rosenthal, 2020. "Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.
    • Handle: RePEc:plo:pone00:0224445
    DOI: 10.1371/journal.pone.0224445
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    References listed on IDEAS

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    1. Pranav Rajpurkar & Jeremy Irvin & Robyn L Ball & Kaylie Zhu & Brandon Yang & Hershel Mehta & Tony Duan & Daisy Ding & Aarti Bagul & Curtis P Langlotz & Bhavik N Patel & Kristen W Yeom & Katie Shpanska, 2018. "Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-17, November.
    2. Ramandeep Singh & Mannudeep K Kalra & Chayanin Nitiwarangkul & John A Patti & Fatemeh Homayounieh & Atul Padole & Pooja Rao & Preetham Putha & Victorine V Muse & Amita Sharma & Subba R Digumarthy, 2018. "Deep learning in chest radiography: Detection of findings and presence of change," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-12, October.
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