Machine learning-assisted flexible wearable device for tyrosine detection

Bao, Qiwen; Li, Gang; Cheng, Weiguo; Yang, Zhengchun; Qu, Zilian; Wei, Jun; Lin, Ling

doi:10.1039/d3ra02900j

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…The whole device is simpler and more miniaturized. Therefore, portable PEC biosensors have a broader application prospect in the field of life analysis [19,20]. Over the past decade, disease diagnostic platforms have advanced in a variety of research areas including magnetic, optical, mechanical and electrochemical sensing systems [21].…”

Section: Introductionmentioning

confidence: 99%

Recent advances on portable photoelectrochemical biosensors for diagnostics

Wu,

Tang

2023

Electroanalysis

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Efficient and accurate diagnostic methods are important for human health monitoring and prevention of major diseases, so the establishment of efficient and convenient testing methods for clinical diagnosis is urgent. Photoelectrochemical (PEC) biosensors have attracted much attention because of their high sensitivity, low detection background, and especially the advantages of combining photoexcitation and electrochemical detection. With the rapid development of integrated circuit technology many wireless transmissions of highly aggregated portable micro devices are manufactured, its small size, easy to carry, and the ability to visualize the results of the test is shown to be the potential for the application of such devices. This review aims to cover recent advances in PEC biosensing within the field of portable detection, including the principles of PEC biosensors, common photovoltaic materials, and scalable fabrication techniques for portable PEC biosensing. Future prospects in the field are also discussed.

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Section: Introductionmentioning

confidence: 99%

Recent advances on portable photoelectrochemical biosensors for diagnostics

Wu,

Tang

2023

Electroanalysis

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Revolutionary Point‐of‐Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Haghayegh,

Norouziazad,

Haghani

et al. 2024

Advanced Science

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Early‐stage disease detection, particularly in Point‐Of‐Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision‐medicine. Public benefits of early detection extend beyond cost‐effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground‐breaking innovations enabling automation of operations, conducting advanced large‐scale data analysis, generating predictive models, and facilitating remote and guided clinical decision‐making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

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Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Bhaiyya,

Panigrahi,

Rewatkar

et al. 2024

ACS Sens.

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Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

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Machine learning-assisted flexible wearable device for tyrosine detection

Abstract: Early diagnosis of pathological markers can significantly shorten the rate of viral transmission, reduce the probability of infection, and improve the cure rate of diseases.

Cited by 4 publications

References 26 publications

Recent advances on portable photoelectrochemical biosensors for diagnostics

Recent advances on portable photoelectrochemical biosensors for diagnostics

Revolutionary Point‐of‐Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

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