Our chest X-ray solution
To detect tuberculosis-related abnormalities in posterior anterior chest X-rays, Thirona developed CAD4TB™.This computer-aided detection software takes a single chest X-ray as its input, in the form of a DICOM image, and produces several outputs: a quality assessment of the input image, a heat map highlighting possible abnormal areas, and a score between 0 and 100 indicating the likelihood of the X-ray being abnormal and the subject on the X-ray being affected by tuberculosis.
How it works
CAD4TB™ has been developed following the principles of deep learning: in the process of computing the score, it compares regions in the input image with regions extracted from normal and abnormal images previously processed by the system, which constitute the so-called training set. One of the conditions for proper supervised learning is that this training set should be representative of the test data; otherwise, results may not be reliable. To fulfill this condition, and thus make CAD4TB™ applicable to diverse scenarios, the system has been trained with data from several populations and several X-ray devices.