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Free Content Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study

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OBJECTIVE: To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients.

MATERIALS AND METHODS: In this prospective, observational study, patients with previously diagnosed TB were enrolled. Photographs of their CXRs were taken using a consumer-grade digital still camera. The images were stratified by pathological patterns into classes: cavity, consolidation, effusion, interstitial changes, miliary pattern or normal examination. Image analysis was performed with commercially available Deep Learning software in two steps. Pathological areas were first localised; detected areas were then classified. Detection was assessed using receiver operating characteristics (ROC) analysis, and classification using a confusion matrix.

RESULTS: The study cohort was 138 patients with human immunodeficiency virus (HIV) and TB co-infection (median age 34 years, IQR 28–40); 54 patients were female. Localisation of pathological areas was excellent (area under the ROC curve 0.82). The software could perfectly distinguish pleural effusions from intraparenchymal changes. The most frequent misclassifications were consolidations as cavitations, and miliary patterns as interstitial patterns (and vice versa).

CONCLUSION: Deep Learning analysis of CXR photographs is a promising tool. Further efforts are needed to build larger, high-quality data sets to achieve better diagnostic performance.
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Keywords: CXR; Deep Learning; TB; chest radiograph; teleradiology

Document Type: Research Article

Affiliations: 1: Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland 2: Infectious Disease Institute, College of Health Sciences, Makerere University, Kampala, Uganda 3: Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland

Publication date: March 1, 2018

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  • The International Journal of Tuberculosis and Lung Disease publishes articles on all aspects of lung health, including public health-related issues such as training programmes, cost-benefit analysis, legislation, epidemiology, intervention studies and health systems research. The IJTLD is dedicated to the continuing education of physicians and health personnel and the dissemination of information on lung health world-wide.

    To share scientific research of immediate concern as rapidly as possible, The Union is fast-tracking the publication of certain articles from the IJTLD and publishing them on The Union website, prior to their publication in the Journal. Read fast-track articles.

    Certain IJTLD articles are also selected for translation into French, Spanish, Chinese or Russian. These are available on the Union website.

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