Detection of Tuberculosis from Exhaled Breath Using Nanomaterial-Based Sensors

Elias Mansour, Chemical Engineering, Technion, Haifa, Israel
Morad Nakhleh, Chemical Engineering, Technion, Haifa, Israel
Raneen Jeries, Chemical Engineering, Technion, Haifa, Israel
Alaa G'arra, Chemical Engineering, Technion, Haifa, Israel
Hossam Haick, Chemical Engineering, Technion, Haifa, Israel

Background: Tuberculosis (TB) is a widespread infectious disease, causing the death of about 2 million patients a year. Conventional diagnosis methods lack accuracy, demand prolonged time until results are received, as well as being highly expensive. Every undiagnosed patient infects a dozen more people, increasing disease spread and death rates.

Aim: the aim of the current study is to test the ability of nanomaterial-based sensors array to diagnose TB, by its unique exhaled breath composition.

Methods: 194 breath samples, obtained of TB patients (n=64), non-TB patients (n=67) and healthy volunteers (n=67), were analyzed using nanomaterial-based sensors and used as a training set for accomplishing diagnostic model. The model’s diagnosis accuracy was then tested by a blind, randomized test that included 60 of the collected samples.

Results: Based on the blind, randomized analysis, the nanomaterial-based sensors array could differentiate between TB and non-TB with a sensitivity of 90%, specificity of 93% and accuracy of 92%. These results were found to be un-affected by smoking habits, HIV status, and/or medication.

Conclusion: nanomaterial-based sensors array could serve as an accurate, non-invasive and inexpensive diagnostic tool for population screening and early detection of TB. Nevertheless, for the TB volatile biomarker testing to become a reality, several advances in the sensor development need to occur, including sensitivity towards low analyte concentration, rapid response to small changes in concentration, and several other aspects relating to both to the properties of the breath itself, and to the structure of the testing system