Comparing deep learning models for tuberculosis detection: A retrospective study of digital vs. analog chest radiographs

Abstract

PurposeChest 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. MethodsA 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. Results10,000 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. ConclusionThe DL-based device underwent evaluation using 10,000 chest X-rays to identify radiological signs of tuberculosis. When tested on the original DICOMs dataset, the device achieved an Area Under the Curve (AUC) of 0.928, with a sensitivity of 0.841 and a specificity of 0.806. Comparing the device's performance with three cellular smartphones on the same original DICOMs dataset, the AUC differences were found to be 0.024 (2.55%), 0.048 (5.10%), and 0.038 (1.91%) at optimal thresholds. These results highlight the device's robustness in detecting radiological signs of TB, showcasing consistent performance across both digital and analog chest X-rays. The smallest difference in AUC further underscores the device's reliability in accurately identifying TB indicators, reaffirming its potential clinical utility.