Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays

Duan, Sixuan; Cai, Tianyu; Zhu, Jing; Yang, Xi; Lim, Eng Gee; Huang, Kaizhu; Hoettges, Kai F.; Zhang, Quan; Fu, Hao; Guo, Qiang; Liu, Xinyu; Yang, Zhangwei; Song, Pengfei

doi:10.1016/j.aca.2023.340868

Cited by 15 publications

(5 citation statements)

References 39 publications

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“…Subsequent advancements include acoustic streaming-based microfluidics, which exhibit potential for multiplexing and automatic immunoassays . Most recently, smartphone-enabled colorimetric ELISA (c-ELISA) presented a fully automated sensing process and user-friendly application …”

Section: Mechanism For Using Microfluidics To Detect Plant Diseasesmentioning

confidence: 99%

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Recent Advances in Microfluidics for the Early Detection of Plant Diseases in Vegetables, Fruits, and Grains Caused by Bacteria, Fungi, and Viruses

Zhao,

Zhai,

Chen

et al. 2024

J. Agric. Food Chem.

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In the context of global population growth expected in the future, enhancing the agri-food yield is crucial. Plant diseases significantly impact crop production and food security. Modern microfluidics offers a compact and convenient approach for detecting these defects. Although this field is still in its infancy and few comprehensive reviews have explored this topic, practical research has great potential. This paper reviews the principles, materials, and applications of microfluidic technology for detecting plant diseases caused by various pathogens. Its performance in realizing the separation, enrichment, and detection of different pathogens is discussed in depth to shed light on its prospects. With its versatile design, microfluidics has been developed for rapid, sensitive, and low-cost monitoring of plant diseases. Incorporating modules for separation, preconcentration, amplification, and detection enables the early detection of trace amounts of pathogens, enhancing crop security. Coupling with imaging systems, smart and digital devices are increasingly being reported as advanced solutions.

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Section: Mechanism For Using Microfluidics To Detect Plant Diseasesmentioning

confidence: 99%

“… 29 Most recently, smartphone-enabled colorimetric ELISA (c-ELISA) presented a fully automated sensing process and user-friendly application. 30 …”

Section: Mechanism For Using Microfluidics To Detect Plant Diseasesmentioning

confidence: 99%

Recent Advances in Microfluidics for the Early Detection of Plant Diseases in Vegetables, Fruits, and Grains Caused by Bacteria, Fungi, and Viruses

Zhao,

Zhai,

Chen

et al. 2024

J. Agric. Food Chem.

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show abstract

“…Nonetheless, benchtop ELISA methods are time-consuming and require high reagent volumes. Consequently, there is a need for automated and miniaturized immunoassay platforms capable of reducing the time and cost of the ELISA process [ 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid Microfluidic Immuno-Biosensor Detection System for the Point-of-Care Determination of High-Sensitivity Urinary C-Reactive Protein

Chen,

Lu,

Tseng

et al. 2024

Biosensors

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A microfluidic immuno-biosensor detection system consisting of a microfluidic spectrum chip and a micro-spectrometer detection device is presented for the rapid point-of-care (POC) detection and quantification of high-sensitivity C-reactive protein (hs-CRP) in urine. The detection process utilizes a highly specific enzyme-linked immunosorbent assay (ELISA) method, in which capture antibodies and detection antibodies are pre-deposited on the substrate of the microchip and used to form an immune complex with the target antigen. Horseradish peroxidase (HRP) is added as a marker enzyme, followed by a colorimetric reaction using 3,3′,5,5′-tetramethylbenzidine (TMB). The absorbance values (a.u.) of the colorimetric reaction compounds are measured using a micro-spectrometer device and used to measure the corresponding hs-CRP concentration according to the pre-established calibration curve. It is shown that the hs-CRP concentration can be determined within 50 min. In addition, the system achieves recovery rates of 93.8–106.2% in blind water samples and 94.5–104.6% in artificial urine. The results showed that the CRP detection results of 41 urine samples from patients with chronic kidney disease (CKD) were highly consistent with the conventional homogeneous particle-enhanced turbidimetric immunoassay (PETIA) method’s detection results (R2 = 0.9910). The experimental results showed its applicability in the detection of CRP in both urine and serum. Overall, the results indicate that the current microfluidic ELISA detection system provides an accurate and reliable method for monitoring the hs-CRP concentration in point-of-care applications.

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“…Biosensors are analytical platforms that can be applied in medical, industrial and agricultural settings, where highly reliable and efficient systems are demanded . In this context, machine learning algorithms, such as neural networks, are enabling fast processing and analysis of massive data, as well as accurate classification and identification of complex patterns, thus offering new capabilities in many fields, including (bio)sensing and diagnostics. − For instance, neural networks have been employed to distinguish patients with leukemia, hepatitis B, or breast cancer from healthy volunteers, respectively . Furthermore, neural networks can be employed to identify drug-dosed living cancer cells and to discriminate bacterial pathogens. − …”

Section: Introductionmentioning

confidence: 99%

AI-Assisted Real-Time Immunoassay Improves Clinical Sensitivity and Specificity

Mancera-Zapata,

Rodríguez-Nava,

Arce

et al. 2024

Anal. Chem.

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Real-time biosensing systems can interrogate the association between the analyte and the biorecognition element across time. Typically, the resulting data are preprocessed to offer valuable bioanalytical information obtained at a single optimal point of such a real-time response; for instance, a diagnosis of certain medical conditions can be established depending on a biomarker (analyte) concentration measured at an optimal time, that is, a threshold. Exploiting this conventional approach, we previously developed a nanophotonic immunoassay for bacterial vaginosis diagnosis exhibiting a clinical sensitivity and specificity of ca. 96.29% (n = 162). Herein, we demonstrate that a real-time biosensing platform assisted by artificial intelligence not only obviates biomarker concentration (i.e., a threshold) determination but also increases sensitivity and specificity in the targeted diagnostic, thereby reaching values of up to 100%.

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Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays

Cited by 15 publications

References 39 publications

Recent Advances in Microfluidics for the Early Detection of Plant Diseases in Vegetables, Fruits, and Grains Caused by Bacteria, Fungi, and Viruses

Recent Advances in Microfluidics for the Early Detection of Plant Diseases in Vegetables, Fruits, and Grains Caused by Bacteria, Fungi, and Viruses

Rapid Microfluidic Immuno-Biosensor Detection System for the Point-of-Care Determination of High-Sensitivity Urinary C-Reactive Protein

AI-Assisted Real-Time Immunoassay Improves Clinical Sensitivity and Specificity

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