An ANN model for the differential diagnosis of tuberculosis and sarcoidosis

Abstract

Sarcoidosis is often misdiagnosed as tuberculosis and consequently mistreated owing to inherent limitations in histopathological and radiological presentations. It is known that the differential diagnosis of Tuberculosis and Sarcoidosis is often non-trivial and requires expertise and experience from clinicians. Therefore, it is of interest to describe a multilayer neural network model to differentiate pulmonary tuberculosis from Sarcoidosis using signal intensity data from blood transcriptional microarray. Genes that are significantly upregulated in Pulmonary Tuberculosis & Sarcoidosis in comparison with healthy controls were used in the model. The model classified Pulmonary Tuberculosis & Sarcoidosis with 95.8% accuracy. The model also helps to identify gene markers that are differentially upregulated in the two clinical conditions.