A Novel Method for Quantitative Analysis of C-Reactive Protein Lateral Flow Immunoassays Images via CMOS Sensor and Recurrent Neural Networks

The utility of smartphone-based quantitative analysis of SARS-CoV-2-specific antibody lateral flow assays

OBJECTIVES

Although antibody measurements using lateral flow assay (LFA) kits are convenient, they usually require a specialized reader for quantification. However, a smartphone-based quantification application can be used as a reader for LFA kits. We investigated the quantification ability of the application for SARS-CoV-2-specific antibodies.

METHODS

Eight hundred frozen serum samples from 100 healthcare professionals who received a COVID-19 vaccine were analyzed. Images of assayed LFA kits were obtained using a smartphone camera. We determined whether the ratio of color density of the test and control lines of spike protein IgG correlated with chemiluminescent immunoassay-measured titers.

RESULTS

Spike protein IgG correlated well with the quantification results of the LFA kits using the application installed on a smartphone (r = 0.886).

CONCLUSION

Our results suggest that smartphone-based quantitative analysis of LFA kits enables the quantification of anti-SARS-CoV-2 IgG without special devices, enabling point-of-care assessment of acquired humoral immunity in various settings.

Advancing diagnostic efficacy using a computer vision-assisted lateral flow assay for influenza and SARS-CoV-2 detection

Lateral flow assays (LFAs) have emerged as indispensable tools for point-of-care testing during the pandemic era. However, the interpretation of results through unassisted visual inspection by untrained individuals poses inherent limitations. In our study, we propose a novel approach that combines computer vision (CV) and lightweight machine learning (ML) to overcome these limitations and significantly enhance the performance of LFAs. By incorporating CV-assisted analysis into the LFA assay, we achieved a remarkable three-fold improvement in analytical sensitivity for detecting Influenza A and for SARS-CoV-2 detection. The obtained R2 values reached approximately 0.95, respectively, demonstrating the effectiveness of our approach. Moreover, the integration of CV techniques with LFAs resulted in a substantial amplification of the colorimetric signal specifically for COVID-19 positive patient samples. Our proposed approach, which incorporates a simple machine learning algorithm, provides substantial enhancements in assay sensitivity, improving diagnostic efficacy and accessibility of point-of-care testing without requiring significant additional resources. Moreover, the simplicity of the machine learning algorithm enables its standalone use on a mobile phone, further enhancing its practicality for point-of-care testing.

Investigating Temporal Features of Carotid Intima-Media Thickness from Ultrasound Imaging with Recurrent Neural Networks

Measuring carotid intima-media thickness (cIMT) of the Common Carotid Artery (CCA) via B-mode ultrasound imaging is a non-invasive yet effective way to monitor and assess cardiovascular risk. Recent studies using Convolutional Neural Networks (CNNs) to automate the process have mainly focused on the detection of regions of interest (ROI) in single frame images collected at fixed time points and have not exploited the temporal information captured in ultrasound imaging. This paper presents a novel framework to investigate the temporal features of cIMT, in which Recurrent Neural Networks (RNN) were deployed for ROI detection using consecutive frames from ultrasound imaging. The cIMT time series can be formed from estimates of cIMT in each frame of an ultrasound scan, from which additional information (such as min, max, mean, and frequency) on cIMT time series can be extracted. Results from evaluation show the best performance for ROI detection improved 4.75% by RNN compared to CNN-based methods. Furthermore, the heart rate estimated from the cIMT time series for seven patients was highly correlated with the patient's clinical records, which suggests the potential application of the cIMT time series and related features for clinical studies in the future.Clinical relevance- The temporal features extracted from cIMT time series provide additional information that can be potentially beneficial for clinical studies.

Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics

BACKGROUND

Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format.

METHODS

We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images.

RESULTS

Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images.

CONCLUSIONS

The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.

A novel patches-selection method for the classification of point-of-care biosensing lateral flow assays with cardiac biomarkers

Cardiovascular Disease (CVD) is amongst the leading cause of death globally, which calls for rapid detection and treatment. Biosensing devices are used for the diagnosis of cardiovascular disease at the point-of-care (POC), with lateral flow assays (LFAs) being particularly useful. However, due to their low sensitivity, most LFAs have been shown to have difficulties detecting low analytic concentrations. Breakthroughs in artificial intelligence (AI) and image processing reduced this detection constraint and improved disease diagnosis. This paper presents a novel patches-selection approach for generating LFA images from the test line and control line of LFA images, analyzing the image features, and utilizing them to reliably predict and classify LFA images by deploying classification algorithms, specifically Convolutional Neural Networks (CNNs). The generated images were supplied as input data to the CNN model, a strong model for extracting crucial information from images, to classify the target images and provide risk stratification levels to medical professionals. With this approach, the classification model produced about 98% accuracy, and as per the literature review, this approach has not been investigated previously. These promising results show the proposed method may be useful for identifying a wide variety of diseases and conditions, including cardiovascular problems.