Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system

Abstract

There is a growing interest in the automated analysis of chest X-Ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis. In this work we evaluate the latest version of CAD4TB, a software platform designed for this purpose. Version 6 of CAD4TB was released in 2018 and is here tested on an independent dataset of 5565 CXR images with GeneXpert (Xpert) sputum test results available (854 Xpert positive subjects). A subset of 500 subjects (50% Xpert positive) was reviewed and annotated by 5 expert observers independently to obtain a radiological reference standard. The latest version of CAD4TB is found to outperform all previous versions in terms of area under receiver operating curve (ROC) with respect to both Xpert and radiological reference standards. Improvements with respect to Xpert are most apparent at high sensitivity levels with a specificity of 76% obtained at 90% sensitivity. When compared with the radiological reference standard, CAD4TB v6 also outperformed previous versions by a considerable margin and achieved 98% specificity at 90% sensitivity. No substantial difference was found between the performance of CAD4TB v6 and any of the various expert observers against the Xpert reference standard. A cost and efficiency analysis on this dataset demonstrates that in a standard clinical situation, operating at 90% sensitivity, users of CAD4TB v6 can process 132 subjects per day at an average cost per screen of $5.95 per subject, while users of version 3 process only 85 subjects per day at a cost of $8.41 per subject. At all tested operating points version 6 is shown to be more efficient and cost effective than any other version.

COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System

“…Some authors are employees (RP, AM, JM) or consultant (BvG) of Thirona, the other authors had control of inclusion of any data and information in this study. CAD4COVID-Xray is based on the CAD4TB v6 software [10], which is a commercial deep-learning system for the detection of tuberculosis on chest radiographs. As pre-processing steps, the system uses image normalization [14] and lung segmentation using a U-net [15].…”

Artificial Intelligence, Radiology, and Tuberculosis: A Review

“…Intriguingly, CAD4TB has now been redeveloped as a deep learning model rather than a CAD program. The latest version of the model, released in 2019, was trained on cohort of 500 labelled images from Pakistan and achieved a sensitivity of 90% and specificity of 98% upon testing for the task of detecting TB, surpassing all previous version of the algorithm (17).…”

Computer-aided covid-19 patient screening using chest images (X-Ray and CT scans)

“…To put these theoretical notions to test, we added bacterial tuberculosis patients to the study. We chose tuberculosis given its importance worldwide (it can actually be considered another pandemic in many countries), availability of images online and also since it can serve as a good baseline for the potential of covid-19 detection, considering that tuberculosis detection from X-Ray images is more established [52][53][54][55] .…”