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

• Published in: NPJ digital medicine Vol. 3; no. 1; p. 115
• Main Authors: Rajpurkar, Pranav, O’Connell, Chloe, Schechter, Amit, Asnani, Nishit, Li, Jason, Kiani, Amirhossein, Ball, Robyn L., Mendelson, Marc, Maartens, Gary, van Hoving, Daniël J., Griesel, Rulan, Ng, Andrew Y., Boyles, Tom H., Lungren, Matthew P.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.09.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 692/700/139; Accuracy; Algorithms; Biomedicine; Biotechnology; Deep learning; Digital technology; Health informatics; HIV; Human immunodeficiency virus; Medical diagnosis; Medicine; Medicine & Public Health; Tuberculosis; X-rays; Machine learning; Diagnosis
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-020-00322-2

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.