Comparative Evaluation of Digital and Analog Chest Radiographs to Identify Tuberculosis using Deep Learning Model

TL;DR

This study evaluates a deep learning device for TB detection on digital and analog chest X-rays, demonstrating its robustness and potential for resource-limited settings with comparable performance across different image formats.

Contribution

It compares the effectiveness of a DL-based TB detection device on digital and analog chest X-rays, including images captured with smartphones, showing consistent performance.

Findings

AUC of 0.928 for digital X-rays in TB detection.

Minimal performance difference across smartphone-captured images.

Robustness of DL device in resource-constrained environments.

Abstract

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.