Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning

TimePAD─Unveiling Temporal Sequence ELISA Signal by Deep Learning for Rapid Readout and Improved Accuracy in a Microfluidic Paper-Based Analytical Platform

The integration of paper-based microfluidics with deep learning represents a pivotal trend in enhancing diagnostic capabilities. This paper introduces a new approach to improve the performance of a paper-based microfluidic enzyme-linked immunosorbent assay (ELISA) by training the temporal sequence colorimetric data rather than static data conventionally, using deep learning. Traditional deep learning-assisted ELISA analysis methods usually rely on a single snapshot of the reaction at its end, which limits the further improvement of sensitivity and specificity (or accuracy for combined evaluation), as it misses dynamic changes in the reaction over time. In this work, we developed a temporal sequence-enhanced paper analytical device (TimePAD) that captures continuous video data of the ELISA reaction, which contains the dynamic colorimetric changes. With the YOLOv8 deep learning alogrithm and the Rabbit IgG as the model for ELISA assay, we can use the initial 20 min signal instead of waiting for 30 min for full reaction, achieving a 33% reduction in the turnaround time. Moreover, the overall accuracy at 20 min is 94.1%, which is slightly improvement to the 93.5% using a traditional single snapshot method at 30 min. This method not only accelerates result interpretation but also enhances the overall efficiency of diagnostics, making it particularly valuable for time-sensitive point-of-care testing applications. Lastly, to demonstrate its real-world use, we expanded to the disease biomarker cTnI detection and obtained accuracy of 98.1% within only 10 min, compared to 25 min with 97.8% accuracy in traditional methods.

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 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.

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection.

RESULTS

This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1-42 (Aβ 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach.

SIGNIFICANCE

This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1-42, particularly in areas with low resources and limited communication infrastructure.

Deep-learning assisted zwitterionic magnetic immunochromatographic assays for multiplex diagnosis of biomarkers

Magnetic nanoparticle (MNP)-based immunochromatographic tests (ICTs) display long-term stability and an enhanced capability for multiplex biomarker detection, surpassing conventional gold nanoparticles (AuNPs) and fluorescence-based ICTs. In this study, we innovatively developed zwitterionic silica-coated MNPs (MNP@Si-Zwit/COOH) with outstanding antifouling capabilities and effectively utilised them for the simultaneous identification of the nucleocapsid protein (N protein) of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) and influenza A/B. The carboxyl-functionalised MNPs with 10% zwitterionic ligands (MNP@Si-Zwit 10/COOH) exhibited a wide linear dynamic detection range and the most pronounced signal-to-noise ratio when used as probes in the ICT. The relative limit of detection (LOD) values were achieved in 12 min by using a magnetic assay reader (MAR), with values of 0.0062 ng/mL for SARS-CoV-2 and 0.0051 and 0.0147 ng/mL, respectively, for the N protein of influenza A and influenza B. By integrating computer vision and deep learning to enhance the image processing of immunoassay results for multiplex detection, a classification accuracy in the range of 0.9672-0.9936 was achieved for evaluating the three proteins at concentrations of 0, 0.1, 1, and 10 ng/mL. The proposed MNP-based ICT for the multiplex diagnosis of biomarkers holds substantial promise for applications in both medical institutions and self-administered diagnostic settings.

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.

Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis

This study presents a groundbreaking approach for the early detection of chronic kidney disease (CKD) and other urological disorders through an image-label-free, multi-dipstick identification method, eliminating the need for complex machinery, label libraries, or preset coordinates. Our research successfully identified reaction pads on 187 multi-dipsticks, each with 11 pads, leveraging machine learning algorithms trained on human urine data. This technique aims to surpass traditional colourimetric methods and concentration-colour curve fitting, offering more robust and precise community screening and home monitoring capabilities. The developed algorithms enhance the generalizability of machine learning models by extracting primary colours and correcting urine colours on each reaction pad. This method's cost-effectiveness and portability are significant, as it requires no additional equipment beyond a standard smartphone. The system's performance rivals professional medical equipment without auxiliary lighting or flash under regular indoor light conditions, effectively managing false positives and negatives across various categories with remarkable accuracy. In a controlled experimental setting, we found that random forest algorithms, based on a Bagging strategy and applied in the HSV colour space, showed optimal results in smartphone-assisted urinalysis. This study also introduces a novel urine colour correction method, significantly improving machine learning model performance. Additionally, ISO parameters were identified as crucial factors influencing the accuracy of smartphone-based urinalysis in the absence of additional lighting or optical configurations, highlighting the potential of this technology in low-resource settings.

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

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs.

Dual-signal readout paper-based wearable biosensor with a 3D origami structure for multiplexed analyte detection in sweat

In this research, we design and implement a small, convenient, and noninvasive paper-based microfluidic sweat sensor that can simultaneously detect multiple key biomarkers in human sweat. The origami structure of the chip includes colorimetric and electrochemical sensing regions. Different colorimetric sensing regions are modified with specific chromogenic reagents to selectively identify glucose, lactate, uric acid, and magnesium ions in sweat, as well as the pH value. The regions of electrochemical sensing detect cortisol in sweat by molecular imprinting. The entire chip is composed of hydrophilically and hydrophobically treated filter paper, and 3D microfluidic channels are constructed by using folding paper. The thread-based channels formed after the hydrophilic and hydrophobic modifications are used to control the rate of sweat flow, which in turn can be used to control the sequence of reactions in the differently developing colored regions to ensure that signals of the best color can be captured simultaneously by the colorimetric sensing regions. Finally, the results of on-body experiments verify the reliability of the proposed sweat sensor and its potential for the noninvasive identification of a variety of sweat biomarkers.

Fabrication of microtiter plate on paper using 96-well plates for wax stamping

Paper-based analytical devices have prominently emerged as a group of diagnostic tools with prospective to eliminate the expensive, time-consuming, and intricate analytical methodologies. Wax printing has been a dominant technique to fabricate hydrophobic patterns on paper for fluid control, but the discontinuation of commercial solid ink printers has begun a genesis of alternate wax patterning strategies. In this study, a simple and rapid fabrication methodology for realizing a 96-well microtiter plate on paper has been developed. The method involves the use of commercially available polystyrene microplates as a stamp for wax patterning. The technique further eradicates the requirement of customized stamps and the step of heating paper substrates for creating wax barriers. Thus, wax stamped paper microplates can be used for a wide range of bioanalytical tests maneuvering reduced generation of non-biodegradable waste, minimal reagent usage, and inexpensive readout strategies. The viability of the fabricated platform has been assessed by colorimetric detection of glutathione using 3,3',5,5'-tetramethylbenzidine-H₂O₂ redox system. RGB analysis of the colorimetric response showed a linear concentration range from 0 to 90 µM (R ² = 0.989) along with a detection limit of 28.375 µM.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10404-022-02606-3.

Point-of-care biochemical assays using electrochemical technologies: approaches, applications, and opportunities

Against the backdrop of hidden symptoms of diseases and limited medical resources of their investigation, in vitro diagnosis has become a popular mode of real-time healthcare monitoring. Electrochemical biosensors have considerable potential for use in wearable products since they can consistently monitor the physiological information of the patient. This review classifies and briefly compares commonly available electrochemical biosensors and the techniques of detection used. Following this, the authors focus on recent studies and applications of various types of sensors based on a variety of methods to detect common compounds and cancer biomarkers in humans. The primary gaps in research are discussed and strategies for improvement are proposed along the dimensions of hardware and software. The work here provides new guidelines for advanced research on and a wider scope of applications of electrochemical biosensors to in vitro diagnosis.