The ability to rapidly and reliably
screen for bacterial vaginosis
(BV) during pregnancy is of great significance for maternal health
and pregnancy outcomes. In this proof-of-concept study, we demonstrated
the potential of carbon nanotube field-effect transistors (NTFET)
in the rapid diagnostics of BV with the sensing of BV-related factors
such as pH and biogenic amines. The fabricated sensors showed good
linearity to pH changes with a linear correlation coefficient of 0.99.
The pH sensing performance was stable after more than one month of
sensor storage. In addition, the sensor was able to classify BV-related
biogenic amine-negative/positive samples with machine learning, utilizing
different test strategies and algorithms, including linear discriminant
analysis (LDA), support vector machine (SVM), and principal component
analysis (PCA). The biogenic amine sample status could be well classified
using a soft-margin SVM model with a validation accuracy of 87.5%.
The accuracy could be further improved using a gold gate electrode
for measurement, with accuracy higher than 90% in both LDA and SVM
models. We also explored the sensing mechanisms and found that the
change in NTFET off current was crucial for classification. The fabricated
sensors successfully detect BV-related factors, demonstrating the
competitive advantage of NTFET for point-of-care diagnostics of BV.