The CAD4TB AI algorithm allows TB programs to use the software offline on a laptop computer and/or in the cloud. In resource constrained areas where internet connectivity is often still unstable or expensive per MB, the offline version of CAD4TB can be the only workable option.
For those settings whereby internet connectivity is stable with an adequate bandwidth and low data bundle cost the Cloud solution can be an alternative for offline use of CAD. The uploading of the automatically compacted dCXR image to the CAD4TB AI algorithm in the cloud normally takes 30-60 seconds and the CAD outputs will be available on the web to the user on site and/or another location anywhere in the World in about 10-15 seconds.
The algorithm outputs are the heat map and CAD abnormality score within about 30 seconds on the laptop that is connected to the digital X-ray system on site. This is also of special operational value in case of outreach screening programs to remote places with poor or no internet connectivity.
Offline solution, no need for internet connectivity
Images will be stored locally and may require offline image back-up provisions
Images can be synchronized with CAD4TB cloud when internet is available
No internet connectivity or hosting cost
For those settings whereby internet connectivity is stable with an adequate bandwidth and low data bundle cost the Cloud solution can be an alternative for offline use of CAD. The uploading of the automatically compacted dCXR image to the CAD AI algorithm in the cloud normally takes 30-50 seconds and the CAD outputs will be available on the web to the user on site and/or another location anywhere in the World in about 10-15 seconds.
Images will be stored online; image back-up can be automated
Data can stay within country using an in-country data server
Posterior Anterior Chest X-ray (PA CXR)
Subject’s age: 4 years or above
Quality assessment of the digital CXR
Abnormality score: 0 -100 + heatmap indicating areas with abnormalities
Higher CAD score higher % positive Xpert® tests
Diagnostic use of CAD
CAD4TB used in automated pre-screening significantly reduces cost and time to diagnose TB for patient and provider . Users can take the above outputs into account in their clinical work: they can decide that the subject should undergo further testing for the presence of TB or other lung diseases in case the heat map displays suspicious regions, verified by a human operator as suspicious, or when the CAD score is above a certain threshold. The user’s choice for this CAD abnormality threshold will depend on the settings in which the software is used. There is a trade-off between sensitivity and specificity (higher sensitivity often means lower specificity) which can also depend on the available TB program budget. So if the budget is limited and the risk group large then selecting a higher CAD score threshold, resulting in less false positives, can allow the program to detect more TB cases, while accepting that some cases will be missed. CAD in addition may alleviate other issues related to CXR interpretation, such as high intra- and inter-observer variability .
CAD as TB risk “traffic light”
So in practice when used as a triage before Xpert®: a presumptive TB patient could for instance be defined as anyone who has a CAD abnormality score of >65 when screened using dCXR. Using CAD in combination with age and gender data enhanced the performance of the software; variations in demographic information can generate different individual risk probabilities for the same CAD4TB scores . The combination of CAD and clinical information can offer improved accuracy and increased specificity compared with using either type of information on its own . In various studies the aptitude of dCXR to detect pre-clinical TB was confirmed. This could also allow TB programs to use CAD as a kind of TB risk traffic light with the following sample thresholds for abnormal, subnormal and normal:
Red 65 < abnormal result Xpert® at nearby hospital lab
Yellow 35 < subnormal result < 65 return in 2-4 weeks for new dCXR/CAD (depending on symptoms)
Green 0 < normal result < 35 no further testing for TB needed
 Nature Scientific Reports July 2015 Automated chest-radiography as a triage for Xpert® testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs. R. H. H. M. Philipsen C. I. Sánchez P. Maduskar J. Melendez L. Peters-Bax J. G. Peter R. Dawson, G. Theron, K. Dheda and B. van Ginneken.
 Van’t Hoog, A. H. et al. A systematic review of the sensitivity and specificity of symptom- and chest-radiography screening for active pulmonary tuberculosis in HIV-negative persons and persons with unknown HIV status. (2013).
 Nature : 29 April 2016 An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information Jaime Melendez et al Scientific Reports volume6, Article number: 25265 (2016).