  Current technologies that facilitate diagnosis for simultaneous detection of
Mycobacterium tuberculosis and its resistance to first-line anti-tuberculosis
drugs (Isoniazid and Rifampicim) are designed for lab-based settings and are
unaffordable for large scale testing implementations. The suitability of a TB
diagnosis instrument, generally required in low-resource settings, to be
implementable in point-of-care last mile public health centres depends on
manufacturing cost, ease-of-use, automation and portability. This paper
discusses a portable, low-cost, machine learning automated Nucleic acid
amplification testing (NAAT) device that employs the use of a smartphone-based
fluorescence detection using novel image processing and chromaticity detection
algorithms. To test the instrument, real time polymerase chain reaction (qPCR)
experiment on cDNA dilution spanning over two concentrations (40 ng/uL and 200
ng/uL) was performed and sensitive detection of multiplexed positive control
assay was verified.
