Abstract
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.
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.
| Original language | English |
|---|---|
| Article number | 115016 |
| Pages (from-to) | 1-9 |
| Number of pages | 9 |
| Journal | Biosensors and Bioelectronics |
| Volume | 223 |
| Early online date | 26 Dec 2022 |
| DOIs | |
| Publication status | Published (in print/issue) - 1 Mar 2023 |
Bibliographical note
Funding Information:This work is the result of the research project (RD-0817786) funded by the Invest NI Connected Health Innovation Center (CHIC).
Funding Information:
We are grateful to Northern Ireland Connected Health Innovation Centre (NI–CHIC) for funding this project, which is supported by Invest Northern Ireland (Invest NI) and the European Union Regional Development Fund (ERDF) .
Publisher Copyright:
© 2022 The Authors
Keywords
- cardiovascular disease
- point of care biosensors
- convolutional neural networks (CNN)
- deep learning
- artificial intelligence
- machine learning
- support vector machine
- Random Forest (RF)
- linear discriminant analysis (LDA)
- image processing
- MATLAB
- sensitivity and specificity
- accuracy
- Deep learning image processing lateral flow assays point-of-care
- Cardiac biomarker convolutional neural networks C-Reactive proteins
