Machine learning in point-of-care testing: innovations, challenges, and opportunities

Abstract

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.

Fig. 1. Overview of ML algorithms and advantages and uses of ML in various POCT platforms: LFAs, VFAs, NAATs, and imaging-based sensors.

a Integration of ML in the SMART^AI-LFA platform enhanced the performance of LFAs by automating result interpretation, improving sensitivity, and enabling accurate predictions. b The TIMESAVER-LFA system demonstrated its high accuracy in distinguishing between positive and negative results while reducing assay time through the prediction algorithm. c xVFA platform used ML-driven diagnostic algorithms to detect Lyme disease using multiplexed antigen panels, leveraging deep learning to classify positive and negative test results. d The multiplexed sensing membrane inside the xVFA cassette features an algorithmically determined layout of immunoreaction spots with seven distinct spotting conditions that specifically interact with the target analyte and gold nanoparticles (AuNPs) conjugates. e ML algorithms applied to xVFA for binary classification and quantification of target biomarker levels. f An on-chip NAAT platform with ML integration for real-time data visualization, time-series analysis, and early prediction of infectious diseases. g A Raman spectroscopy sensor-based NAAT platform incorporating ML for RNA sensing and deep learning to improve the accuracy of classifications for infectious disease diagnosis. h Smartphone-enabled DNA testing for malaria detection used deep learning to assist with local decision-making and blockchain technology to enhance data security. i A holographic imaging prototype for label-free live plaque assay to quantify virus. j A custom-made, 3D-printed smartphone-based bright-field microscope. a, b, d, e, h, i These are adapted with permission from refs. ^,,,, by Springer Nature; c, g These are adapted with permission from ref. ^, by American Chemical Society; f, j These are adapted with permission from ref. ^, by Elsevier.

Fig. 2. Application of ML to LFAs.

a Infographics illustrating the benefits of HIV LFA processing in field settings (top), and HIV LFA capturing and analysis procedures used to enhance sensitivity and specificity for the detection of HIV in field settings (bottom). b LFA processing pipeline used for the automated interpretation of the REACT-2 dataset. c The SMART^AI-LFA platform used deep learning for automated image processing and result interpretation, utilizing this technology to detect the presence of the SARS-CoV-2 antigen. d The TIMESAVER-LFA platform employed an AI-based verification algorithm to reduce assay time to 1–2 min. e The fluorescent UCNP-based LFA used transfer learning to enhance sensitivity and quantification capability for the detection of methamphetamine and morphine. f An ML approach was used in MNP-based LFA to improve analytical performance for the detection of hCG and multiple cardiac biomarkers. g The PDA nanoparticle-based LFA for the detection of COVID-19 neutralizing antibodies offered precise quantification of antibody concentrations through AI-based analysis. a–d, f These are adapted with permission from refs. ^,,,,, by Springer Nature; e This is adapted with permission from ref. by Elsevier; g This is adapted with permission from ref. by Elsevier.

Fig. 3. Applications of ML to VFAs.

a SERS-based VFA processed by linear regression models for quantitative detection of inflammation markers. b Colorimetric ELISA (c-ELISA) processed by neural networks for accurate detection of Rabbit IgG under different illumination conditions. c Deep learning-enhanced xVFA for high-sensitivity detection of cTnI. d Peptide-based xVFA processed by deep learning models for single-tier testing of Lyme disease. a This is adapted with permission from ref. by Wiley; b This is adapted with permission from ref. by Elsevier; c This is adapted with permission from ref. by American Chemical Society; d This is adapted with permission from ref. by Springer Nature.

Fig. 4. Applications of ML to NAATs.

a A hand-held AI-LAMP device for rapid detection of COVID-19 with AI-based image analysis reduced the sample-to-answer time and improved signal interpretation. b A one-step smartphone-based colorimetric RT-LAMP COVID-19 screening method enabled by pH-sensitive dyes and a transformer AI model. c A lab-on-chip nucleic acid amplification device that utilized ISFET arrays and a spectrogram-based CNN to classify COVID-19 and three cancer biomarkers, featuring a compact size and improved accuracy. d An AI-aided on-chip µPAD for COVID-19 detection using three neural network models (i.e., RNN, LSTM, and GRU) for qualitative analysis. a This is adapted with permission from ref. by MDPI; b–d These are adapted with permission from refs. ^,,, respectively, by Elsevier.

Fig. 5. Applications of ML to imaging-based sensors.

a Image of the holographic microscopy system for bacteria detection (left), whole agar plate image of mixed E. coli and K. aerogenes colonies (middle), and amplitude and phase images of the individual growing colonies (right). b Image of the holographic system for PFU imaging (left), whole plate comparison between the stain-free viral plaque assay after 15 h and the traditional plaque assay after 48 h (right). c Image of the field-portable lens-free imaging flow cytometer (left), which can be used for water quality monitoring and to detect parasites in bodily fluids; the reconstructed images of different microplankton species captured using this portable imaging cytometer (right). d Image of the smartphone-based fluorescent microscope with the disposable sample cassette (top), and schematic illustration of the emission and excitation paths (bottom). e Schematic illustration of the Sight OLO hematology analyzer (top), and false-colored micrographs of different anomalous cell types and formations captured by OLO, red channel: hemoglobin absorption; green channel: nuclear DNA fluorescence; blue channel: cytoplasmic staining (bottom). f Schematic illustration of the miniaturized microscope for automated blood analysis (top), and AI-driven quantification pipeline for FWD, RBC, and MCH count (bottom). a This is adopted with permission from ref. by LSA; b This is adapted from ref. with permission from Nature BME; c This is adapted with permission from ref. by LSA; d This is adapted with permission from ref. by Lab on a Chip; e This is adapted with permission from ref. by Wiley; f This is adapted with permission from ref. by Analyst.