CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV
| Autor: | Amirhossein Kiani, Amit Schechter, Tom H. Boyles, Matthew P. Lungren, Robyn L. Ball, Gary Maartens, Andrew Y. Ng, Marc Mendelson, Chloe O'Connell, Pranav Rajpurkar, Nishit Asnani, Rulan Griesel, Daniël J. van Hoving, Jason Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Tuberculosis Computer applications to medicine. Medical informatics Human immunodeficiency virus (HIV) R858-859.7 Medicine (miscellaneous) Health Informatics Economic shortage lcsh:Computer applications to medicine. Medical informatics medicine.disease_cause Article 03 medical and health sciences 0302 clinical medicine Health Information Management Diagnosis Machine learning Clinical information Medicine In patient 030212 general & internal medicine Preventable death business.industry medicine.disease Computer Science Applications Radiological weapon Emergency medicine lcsh:R858-859.7 business 030217 neurology & neurosurgery Co infection |
| Zdroj: | npj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020) NPJ Digital Medicine |
| ISSN: | 2398-6352 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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