A smartphone-integrated deep learning strategy-assisted rapid detection system for monitoring dual-modal immunochromatographic assay

This study focuses on the integration of a custom-built and optimally trained YOLO v5 model into a smartphone app developed with Java language. A dual-modal immunochromatographic rapid detection system based on a deep learning strategy for smartphones was developed for grade determination and predic...

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Published inTalanta (Oxford) Vol. 282; p. 127043
Main Authors Sun, Qingwen, Feng, Shaoqing, Xu, Hao, Yu, Ruoyao, Dai, Bin, Guo, Jinhong, Fang, Mengru, Cui, Daxiang, Wang, Kan
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier B.V 01.01.2025
Subjects
Aflatoxin B1
Deep learning
Immunochromatography
Smartphone
YOLO v5
Deep learning
Aflatoxin B1
YOLO v5
Immunochromatography
Smartphone
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Abstract This study focuses on the integration of a custom-built and optimally trained YOLO v5 model into a smartphone app developed with Java language. A dual-modal immunochromatographic rapid detection system based on a deep learning strategy for smartphones was developed for grade determination and predicting the concentration of aflatoxin B1 (AFB1). Innovative distance-type quantum dot microsphere fluorescent immunochromatographic chips enable semi-quantitative analysis by naked eye, and conventional colloidal gold nanoparticle colorimetric strips were also prepared. The compact and versatile hardware device making it easily integrable into smartphones of varying dimensions. Moreover, the wireless charging functionality of smartphones was to tackle power supply challenges. After optimized training, the accuracy, mAP@0.5, precision, and recall metrics of the YOLO v5 model all soared to 98 %. For the dual-modal immunochromatographic chips, the R2 values for the standard curve fits were as high as 0.993, with a broad linear range of 0.05–40 ng/mL and a standard deviation lower than 0.03 at each concentration. Finally, this system determined the grade of the AFB1 concentration with an accuracy of up to 98 % and it exhibited an ultra-sensitive quantitative detection capability with a limit of detection as low as 2.2 pg/mL, showcasing the reliability of the deep learning strategy for practical applications in smartphones. This robust technological foundation paves the way for potentially community-based, family-oriented, and personalized applications. [Display omitted] •AI-assisted smartphone-based system enables offline and rapid detection.•Innovative distance-type LFIA chip facilitates the detection signal readout.•Adjustable smartphone-based hardware device enables wireless power supply.
AbstractList This study focuses on the integration of a custom-built and optimally trained YOLO v5 model into a smartphone app developed with Java language. A dual-modal immunochromatographic rapid detection system based on a deep learning strategy for smartphones was developed for grade determination and predicting the concentration of aflatoxin B1 (AFB1). Innovative distance-type quantum dot microsphere fluorescent immunochromatographic chips enable semi-quantitative analysis by naked eye, and conventional colloidal gold nanoparticle colorimetric strips were also prepared. The compact and versatile hardware device making it easily integrable into smartphones of varying dimensions. Moreover, the wireless charging functionality of smartphones was to tackle power supply challenges. After optimized training, the accuracy, mAP@0.5, precision, and recall metrics of the YOLO v5 model all soared to 98 %. For the dual-modal immunochromatographic chips, the R2 values for the standard curve fits were as high as 0.993, with a broad linear range of 0.05-40 ng/mL and a standard deviation lower than 0.03 at each concentration. Finally, this system determined the grade of the AFB1 concentration with an accuracy of up to 98 % and it exhibited an ultra-sensitive quantitative detection capability with a limit of detection as low as 2.2 pg/mL, showcasing the reliability of the deep learning strategy for practical applications in smartphones. This robust technological foundation paves the way for potentially community-based, family-oriented, and personalized applications.This study focuses on the integration of a custom-built and optimally trained YOLO v5 model into a smartphone app developed with Java language. A dual-modal immunochromatographic rapid detection system based on a deep learning strategy for smartphones was developed for grade determination and predicting the concentration of aflatoxin B1 (AFB1). Innovative distance-type quantum dot microsphere fluorescent immunochromatographic chips enable semi-quantitative analysis by naked eye, and conventional colloidal gold nanoparticle colorimetric strips were also prepared. The compact and versatile hardware device making it easily integrable into smartphones of varying dimensions. Moreover, the wireless charging functionality of smartphones was to tackle power supply challenges. After optimized training, the accuracy, mAP@0.5, precision, and recall metrics of the YOLO v5 model all soared to 98 %. For the dual-modal immunochromatographic chips, the R2 values for the standard curve fits were as high as 0.993, with a broad linear range of 0.05-40 ng/mL and a standard deviation lower than 0.03 at each concentration. Finally, this system determined the grade of the AFB1 concentration with an accuracy of up to 98 % and it exhibited an ultra-sensitive quantitative detection capability with a limit of detection as low as 2.2 pg/mL, showcasing the reliability of the deep learning strategy for practical applications in smartphones. This robust technological foundation paves the way for potentially community-based, family-oriented, and personalized applications.
This study focuses on the integration of a custom-built and optimally trained YOLO v5 model into a smartphone app developed with Java language. A dual-modal immunochromatographic rapid detection system based on a deep learning strategy for smartphones was developed for grade determination and predicting the concentration of aflatoxin B1 (AFB1). Innovative distance-type quantum dot microsphere fluorescent immunochromatographic chips enable semi-quantitative analysis by naked eye, and conventional colloidal gold nanoparticle colorimetric strips were also prepared. The compact and versatile hardware device making it easily integrable into smartphones of varying dimensions. Moreover, the wireless charging functionality of smartphones was to tackle power supply challenges. After optimized training, the accuracy, mAP@0.5, precision, and recall metrics of the YOLO v5 model all soared to 98 %. For the dual-modal immunochromatographic chips, the R2 values for the standard curve fits were as high as 0.993, with a broad linear range of 0.05–40 ng/mL and a standard deviation lower than 0.03 at each concentration. Finally, this system determined the grade of the AFB1 concentration with an accuracy of up to 98 % and it exhibited an ultra-sensitive quantitative detection capability with a limit of detection as low as 2.2 pg/mL, showcasing the reliability of the deep learning strategy for practical applications in smartphones. This robust technological foundation paves the way for potentially community-based, family-oriented, and personalized applications. [Display omitted] •AI-assisted smartphone-based system enables offline and rapid detection.•Innovative distance-type LFIA chip facilitates the detection signal readout.•Adjustable smartphone-based hardware device enables wireless power supply.
This study focuses on the integration of a custom-built and optimally trained YOLO v5 model into a smartphone app developed with Java language. A dual-modal immunochromatographic rapid detection system based on a deep learning strategy for smartphones was developed for grade determination and predicting the concentration of aflatoxin B1 (AFB1). Innovative distance-type quantum dot microsphere fluorescent immunochromatographic chips enable semi-quantitative analysis by naked eye, and conventional colloidal gold nanoparticle colorimetric strips were also prepared. The compact and versatile hardware device making it easily integrable into smartphones of varying dimensions. Moreover, the wireless charging functionality of smartphones was to tackle power supply challenges. After optimized training, the accuracy, mAP@0.5, precision, and recall metrics of the YOLO v5 model all soared to 98 %. For the dual-modal immunochromatographic chips, the R values for the standard curve fits were as high as 0.993, with a broad linear range of 0.05-40 ng/mL and a standard deviation lower than 0.03 at each concentration. Finally, this system determined the grade of the AFB1 concentration with an accuracy of up to 98 % and it exhibited an ultra-sensitive quantitative detection capability with a limit of detection as low as 2.2 pg/mL, showcasing the reliability of the deep learning strategy for practical applications in smartphones. This robust technological foundation paves the way for potentially community-based, family-oriented, and personalized applications.
ArticleNumber 127043
Author Wang, Kan
Xu, Hao
Dai, Bin
Sun, Qingwen
Yu, Ruoyao
Cui, Daxiang
Fang, Mengru
Feng, Shaoqing
Guo, Jinhong
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Immunochromatography
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Snippet This study focuses on the integration of a custom-built and optimally trained YOLO v5 model into a smartphone app developed with Java language. A dual-modal...
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SubjectTerms Aflatoxin B1
Deep learning
Immunochromatography
Smartphone
YOLO v5
Title A smartphone-integrated deep learning strategy-assisted rapid detection system for monitoring dual-modal immunochromatographic assay
URI https://dx.doi.org/10.1016/j.talanta.2024.127043
https://www.ncbi.nlm.nih.gov/pubmed/39406103
https://www.proquest.com/docview/3117072660
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