Deep learning on lateral flow immunoassay for the analysis of detection data
Lateral flow immunoassay (LFIA) is an important detection method in vitro diagnosis, which has been widely used in medical industry. It is difficult to analyze all peak shapes through classical methods due to the complexity of LFIA. Classical methods are generally some peak-finding methods, which ca...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2023.1091180/full |
| _version_ | 1797942777486508032 |
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| author | Xinquan Liu Kang Du Si Lin Si Lin Yan Wang |
| author_facet | Xinquan Liu Kang Du Si Lin Si Lin Yan Wang |
| author_sort | Xinquan Liu |
| collection | DOAJ |
| description | Lateral flow immunoassay (LFIA) is an important detection method in vitro diagnosis, which has been widely used in medical industry. It is difficult to analyze all peak shapes through classical methods due to the complexity of LFIA. Classical methods are generally some peak-finding methods, which cannot distinguish the difference between normal peak and interference or noise peak, and it is also difficult for them to find the weak peak. Here, a novel method based on deep learning was proposed, which can effectively solve these problems. The method had two steps. The first was to classify the data by a classification model and screen out double-peaks data, and second was to realize segmentation of the integral regions through an improved U-Net segmentation model. After training, the accuracy of the classification model for validation set was 99.59%, and using combined loss function (WBCE + DSC), intersection over union (IoU) value of segmentation model for validation set was 0.9680. This method was used in a hand-held fluorescence immunochromatography analyzer designed independently by our team. A Ferritin standard curve was created, and the T/C value correlated well with standard concentrations in the range of 0–500 ng/ml (R2 = 0.9986). The coefficients of variation (CVs) were ≤ 1.37%. The recovery rate ranged from 96.37 to 105.07%. Interference or noise peaks are the biggest obstacle in the use of hand-held instruments, and often lead to peak-finding errors. Due to the changeable and flexible use environment of hand-held devices, it is not convenient to provide any technical support. This method greatly reduced the failure rate of peak finding, which can reduce the customer’s need for instrument technical support. This study provided a new direction for the data-processing of point-of-care testing (POCT) instruments based on LFIA. |
| first_indexed | 2024-04-10T20:13:58Z |
| format | Article |
| id | doaj.art-dd35dbdf20fb45e595b839ca4d49b5d9 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-10T20:13:58Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-dd35dbdf20fb45e595b839ca4d49b5d92023-01-26T08:11:04ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882023-01-011710.3389/fncom.2023.10911801091180Deep learning on lateral flow immunoassay for the analysis of detection dataXinquan Liu0Kang Du1Si Lin2Si Lin3Yan Wang4School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaTianjin Boomscience Technology Co., Ltd., Tianjin, ChinaSchool of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaBeijing Savant Biotechnology Co., Ltd., Beijing, ChinaSchool of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaLateral flow immunoassay (LFIA) is an important detection method in vitro diagnosis, which has been widely used in medical industry. It is difficult to analyze all peak shapes through classical methods due to the complexity of LFIA. Classical methods are generally some peak-finding methods, which cannot distinguish the difference between normal peak and interference or noise peak, and it is also difficult for them to find the weak peak. Here, a novel method based on deep learning was proposed, which can effectively solve these problems. The method had two steps. The first was to classify the data by a classification model and screen out double-peaks data, and second was to realize segmentation of the integral regions through an improved U-Net segmentation model. After training, the accuracy of the classification model for validation set was 99.59%, and using combined loss function (WBCE + DSC), intersection over union (IoU) value of segmentation model for validation set was 0.9680. This method was used in a hand-held fluorescence immunochromatography analyzer designed independently by our team. A Ferritin standard curve was created, and the T/C value correlated well with standard concentrations in the range of 0–500 ng/ml (R2 = 0.9986). The coefficients of variation (CVs) were ≤ 1.37%. The recovery rate ranged from 96.37 to 105.07%. Interference or noise peaks are the biggest obstacle in the use of hand-held instruments, and often lead to peak-finding errors. Due to the changeable and flexible use environment of hand-held devices, it is not convenient to provide any technical support. This method greatly reduced the failure rate of peak finding, which can reduce the customer’s need for instrument technical support. This study provided a new direction for the data-processing of point-of-care testing (POCT) instruments based on LFIA.https://www.frontiersin.org/articles/10.3389/fncom.2023.1091180/fulllateral flow immunoassaydata processingpoint of care testingdeep learningconvolutional neural networkU-Net model |
| spellingShingle | Xinquan Liu Kang Du Si Lin Si Lin Yan Wang Deep learning on lateral flow immunoassay for the analysis of detection data Frontiers in Computational Neuroscience lateral flow immunoassay data processing point of care testing deep learning convolutional neural network U-Net model |
| title | Deep learning on lateral flow immunoassay for the analysis of detection data |
| title_full | Deep learning on lateral flow immunoassay for the analysis of detection data |
| title_fullStr | Deep learning on lateral flow immunoassay for the analysis of detection data |
| title_full_unstemmed | Deep learning on lateral flow immunoassay for the analysis of detection data |
| title_short | Deep learning on lateral flow immunoassay for the analysis of detection data |
| title_sort | deep learning on lateral flow immunoassay for the analysis of detection data |
| topic | lateral flow immunoassay data processing point of care testing deep learning convolutional neural network U-Net model |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2023.1091180/full |
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