Artificial Intelligence and Machine Learning in Sensor-based Virus Detection

Sensor-based virus detection demands ultra-low limits of detection (LoD), short time-to-result (TTR), and robustness across samples and devices. This chapter surveys how artificial intelligence and machine learning (AI/ML) convert raw biosensor signals—fluorescence kinetics from clustered regularly interspaced short palindromic repeat (CRISPR) assays, electrochemical impedance spectra, lateral-flow images, field-effect transistor (FET) transfer curves, nanopore ionic-current traces, and even smartphone audio—into calibrated decisions on infection status, viral load, and variant. An end-to-end sensor→aritificial intelligence (AI) pipeline: nanomaterial transduction; preprocessing for drift, illumination, and baseline artifacts; and supervised models that output probabilities with uncertainty are outlined. Deep architectures [one-dimensional convolutional neural networks (1D-CNNs), long short-term memories (LSTMs), transformers] learn features directly; semi/self-supervised and federated learning exploit weak labels and multi-site data; Bayesian and reinforcement strategies optimize assay conditions to reduce TTR and improve separability. Evaluation aligned to clinical use—sensitivity/specificity, positive predictive value/negative predictive value (PPV/NPV) at prevalence, LoD, calibration, and external validation—is emphasized, alongside remedies for label noise, imbalance, and domain shift. Case studies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza, respiratory syncytial virus (RSV), and dengue illustrate gains in sensitivity and reliability, and the chapter concludes with deployment, ethics, and reproducibility.