Qualitative analysis of biological tuberculosis samples by an electronic nose-based artificial neural network

Abstract

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.

Diagnosis of tuberculosis through breath test: A systematic review

“…Jacinto Convit”, Venezuela88 (73–97)94 (83–99)McNerney et al [20]60 (31 PTB, 29 non-TB)Patients with respiratory complaints; Adults (≥17 years)Outpatient clinic of a hospital, EthiopiaCross-sectional, prospective. Consecutive enrolment: unclear.RBS Breathalyzer (immunosensor and bio-optical technology)Combination of clinical assessment, SSM, and CXRSeparate plastic tubesImmunosensor (coated prism with TB antibodies labeled with a fluorescent dye)Not mentionedEvanescent wave fluorimetryTARGETS Communicable Diseases Research Consortium funded by Department for International Development, UK74 (55–88)79 (60–92)van Beek et al [36]226 (90 TB, 136 healthy controls)Culture-confirmed TB patients and healthy controls; Adults (≥ 18 years)Lung Hospital and construction company, VietnamCase-control, prospective. Consecutive enrolment: yes.eNO (Nitric Oxide) analyserLJ CultureInside the deviceN.A.Negative culture, difficulty to breath, no CXR, HIV(+), current TB treatment.The concentration of NO was noted in ppb.Not mentioned78 (68–86)62 (47–75)Phillips et al [18]251 (130 PTB, 121 healthy controls)PTB patients and healthy controls; Children and adults (≥13 years)Hospitals and Health Sciences Institute in Phillipines, United Kingdom, and IndiaCase-control, prospective.…”

“…Five studies only included subjects representing two extreme sides of the clinical spectrum of TB disease (healthy controls without any TB symptoms, and symptomatic, treatment-naïve, smear positive pulmonary TB cases) [18,21,[36], [37], [38]]. Meanwhile, different stages of TB disease are characterized by dynamic metabolic changes within pathogen and host and their interactions, thus different stages of TB disease may generate different breath test results [21].…”

“…In several electronic-nose studies, the exhaled breath was collected into a bag/tube with the specimen investigated later [19,21,22,36]. Using bags for collection may be more useful in the flow of healthcare in a clinic, however, it might change the composition of the sample because the VOCs bind with the sample-bag material, and the bag material releases background emission of several pollutants [52].…”

“…The device performance can be assessed more objectively if the next stage (validation phase) is carried out in subjects who are not involved in the training phase. In only three out of six studies with electronic-nose, the validation phase used independent subjects from the training phase [19,21,36]. In addition, a large number of subjects in the training phase is of great importance to expose the device with large diversity in breath patterns from true-positive study subjects.…”

Sensitivity and specificity of an electronic nose in diagnosing pulmonary tuberculosis among patients with suspected tuberculosis

“…The study in Bangladesh used a prototype of the e-nose (participants exhaled into a bag, then the bag content was examined using a laboratory version of the e-nose), and with smaller sample size [11]. Other studies with e-nose devices did not diagnose PTB among patients with suspected PTB [12,24,25]. The sensitivity in our study was modest, while specificity was low.…”