Using Machine Learning and Optical Microscopy Image Analysis of Immunosensors Made on Plasmonic Substrates: Application to Detect the SARS-CoV-2 Virus.

ABSTRACT: In this article, we introduce a diagnostic platform comprising an optical microscopy image analysis system coupled with machine learning. Its efficacy is demonstrated in detecting SARS-CoV-2 virus particles at concentrations as low as 1 PFU (plaque-forming unit) per milliliter by processing images from an immunosensor on a plasmonic substrate. This high performance was achieved by classifying images with the support vector machine (SVM) algorithm and the MobileNetV3_small convolutional neural network (CNN) model, which attained an accuracy of 91.6% and a specificity denoted by an F1 score of 96.9% for the negative class. Notably, this approach enabled the detection of SARS-CoV-2 concentrations 1000 times lower than the limit of detection achieved with localized surface plasmon resonance (L

SUBMITTER: Oiticica PRA 

PROVIDER: S-EPMC11877505 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature