Imagine you’re trying to find your way through a jungle. Some paths are clear and lead straight to your destination, but others are obstructed by branches, leaves, and other obstacles. Well, that’s a bit like the challenges scientists face when analyzing data from lateral flow immunoassays (LFIA) – an important method used in medical diagnosis. Traditional methods can struggle to distinguish between normal peaks and interference or noise peaks, and they may even miss weak peaks altogether. But fear not! A team of clever researchers has come up with a solution: deep learning! They developed a two-step method using a classification model to identify double-peaks and an improved segmentation model to separate integral regions. After training, their models achieved impressive accuracy levels. To put their method to the test, they used it in a user-friendly hand-held fluorescence immunochromatography analyzer. The results were outstanding, with the method successfully detecting Ferritin concentrations within a range of 0-500 ng/ml. By reducing the failure rate of peak finding and minimizing the need for technical support, this study opens up exciting possibilities for improving point-of-care testing instruments based on LFIA. Curious to learn more? Dive into the research!

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.

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