Comparative Evaluation of Digital and Analog Chest Radiographs to
  Identify Tuberculosis using Deep Learning Model
Subhankar Chattoraj Bhargava Reddy Manoj Tadepalli Preetham Putha
Purpose:Chest X-ray (CXR) is an essential tool and one of the most prescribed
imaging to detect pulmonary abnormalities, with a yearly estimate of over 2
billion imaging performed worldwide. However, the accurate and timely diagnosis
of TB remains an unmet goal. The prevalence of TB is highest in
low-middle-income countries, and the requirement of a portable, automated, and
reliable solution is required. In this study, we compared the performance of
DL-based devices on digital and analog CXR. The evaluated DL-based device can
be used in resource-constraint settings. Methods: A total of 10,000 CXR
DICOMs(.dcm) and printed photos of the films acquired with three different
cellular phones - Samsung S8, iPhone 8, and iPhone XS along with their
radiological report were retrospectively collected from various sites across
India from April 2020 to March 2021. Results: 10,000 chest X-rays were utilized
to evaluate the DL-based device in identifying radiological signs of TB. The
AUC of qXR for detecting signs of tuberculosis on the original DICOMs dataset
was 0.928 with a sensitivity of 0.841 at a specificity of 0.806. At an optimal
threshold, the difference in the AUC of three cellular smartphones with the
original DICOMs is 0.024 (2.55%), 0.048 (5.10%), and 0.038 (1.91%). The minimum
difference demonstrates the robustness of the DL-based device in identifying
radiological signs of TB in both digital and analog CXR.