Classification of nucleic acid amplification on ISFET arrays using spectrogram-based neural networks.

ABSTRACT: The COVID-19 pandemic has highlighted a significant research gap in the field of molecular diagnostics. This has brought forth the need for AI-based edge solutions that can provide quick diagnostic results whilst maintaining data privacy, security and high standards of sensitivity and specificity. This paper presents a novel proof-of-concept method to detect nucleic acid amplification using ISFET sensors and deep learning. This enables the detection of DNA and RNA on a low-cost and portable lab-on-chip platform for identifying infectious diseases and cancer biomarkers. We show that by using spectrograms to transform the signal to the time-frequency domain, image processing techniques can be applied to achieve the reliable classification of the detected chemical signals. Transformation to s

SUBMITTER: Tripathi P 

PROVIDER: S-EPMC10176963 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature