Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models
Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artifici...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/14/12/611 |
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| author | Towfeeq Fairooz Sara E. McNamee Dewar Finlay Kok Yew Ng James McLaughlin |
| author_facet | Towfeeq Fairooz Sara E. McNamee Dewar Finlay Kok Yew Ng James McLaughlin |
| author_sort | Towfeeq Fairooz |
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| description | Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A modified Gray-Level Co-occurrence Matrix, termed the Averaged Horizontal Multiple Offsets Gray-Level Co-occurrence Matrix, was utilised to compute the textural features of the biosensor assay images. Significant textural features were selected for further analysis. A deep learning Convolutional Neural Network model was employed to extract features from these textural features. Both traditional machine learning models and hybrid artificial intelligence models, which combine Convolutional Neural Network features with traditional algorithms, were used to categorise these textural features based on the thyroid-stimulating hormone concentration levels. The proposed method achieved accuracy levels exceeding 95%. This pioneering study highlights the utility of textural aspects of assay images for accurate predictive disease modelling, offering promising advancements in diagnostics and management within biomedical research. |
| format | Article |
| id | doaj-art-27d7ae810016481cb98d1b2feefa5169 |
| institution | Kabale University |
| issn | 2079-6374 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj-art-27d7ae810016481cb98d1b2feefa51692024-12-27T14:14:16ZengMDPI AGBiosensors2079-63742024-12-01141261110.3390/bios14120611Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI ModelsTowfeeq Fairooz0Sara E. McNamee1Dewar Finlay2Kok Yew Ng3James McLaughlin4School of Engineering, Ulster University, Belfast BT15 1ED, UKSchool of Engineering, Ulster University, Belfast BT15 1ED, UKSchool of Engineering, Ulster University, Belfast BT15 1ED, UKSchool of Engineering, Ulster University, Belfast BT15 1ED, UKSchool of Engineering, Ulster University, Belfast BT15 1ED, UKLateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A modified Gray-Level Co-occurrence Matrix, termed the Averaged Horizontal Multiple Offsets Gray-Level Co-occurrence Matrix, was utilised to compute the textural features of the biosensor assay images. Significant textural features were selected for further analysis. A deep learning Convolutional Neural Network model was employed to extract features from these textural features. Both traditional machine learning models and hybrid artificial intelligence models, which combine Convolutional Neural Network features with traditional algorithms, were used to categorise these textural features based on the thyroid-stimulating hormone concentration levels. The proposed method achieved accuracy levels exceeding 95%. This pioneering study highlights the utility of textural aspects of assay images for accurate predictive disease modelling, offering promising advancements in diagnostics and management within biomedical research.https://www.mdpi.com/2079-6374/14/12/611convolutional neural network (CNN)lateral flow assay (LFA)point-of-care (POC)gray-level co-occurrence matrix (GLCM)region of interest (ROI)texture analysis |
| spellingShingle | Towfeeq Fairooz Sara E. McNamee Dewar Finlay Kok Yew Ng James McLaughlin Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models Biosensors convolutional neural network (CNN) lateral flow assay (LFA) point-of-care (POC) gray-level co-occurrence matrix (GLCM) region of interest (ROI) texture analysis |
| title | Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models |
| title_full | Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models |
| title_fullStr | Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models |
| title_full_unstemmed | Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models |
| title_short | Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models |
| title_sort | enhancing sensitivity of point of care thyroid diagnosis via computational analysis of lateral flow assay images using novel textural features and hybrid ai models |
| topic | convolutional neural network (CNN) lateral flow assay (LFA) point-of-care (POC) gray-level co-occurrence matrix (GLCM) region of interest (ROI) texture analysis |
| url | https://www.mdpi.com/2079-6374/14/12/611 |
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