ABSTRACT
OBJECTIVE: To apply an e-nose system for monitoring headspace volatiles in biological samples from Egyptian patients with active pulmonary tuberculosis (TB) and healthy controls (HCs) and compare them with standard sputum analysis. DESIGN: The study population comprised 260 (140 males, 120 females) newly diagnosed TB patients and 240 (120 males, 120 females) HCs matched by age and socio-economic level admitted to hospitals specialising in chest diseases in Alexandria, Behera, Giza and Damietta Governorates, Egypt. Participants provided a history of TB and underwent clinical examinations, chest X-ray, and microbiological and e-nose analyses. Biological samples (blood, breath, sputum and urine) were collected. RESULTS AND CONCLUSION: Being a confirmed TB patient was directly proportional to e-nose 10-sensor responses. Principal component analysis clusters showed a clear distinction between TB and HC groups, with variances of 93%, 85%, 75% and 95% for blood, breath, sputum and urine samples, respectively. Overall accuracy, sensitivity and specificity of the artificial neural network (ANN) analysis for classifying samples were >99%. The e-nose successfully distinguished TB patients from HC participants for all measured biological samples with great precision. With urine samples gaining broader acceptance for clinical diagnosis, an e-nose-based ANN can be a very useful tool for low-cost mass screening and early detection of TB patients in developing countries.
