Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Zhang, Kaiyi; Wang, Jianwu; Liu, Tianyi; Luo, Yifei; Loh, Xian Jun; Chen, Xiaodong

doi:10.1002/adhm.202100734

Cited by 97 publications

(47 citation statements)

References 199 publications

(134 reference statements)

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“…In addition, the growing field of chemometrics, machine learning and artificial intelligence (AI), through multivariate approaches, could represent a crucial advance toward the development of both optimized platforms and the application towards multiplexing. [61][62][63][64] In summary, SPE technology is a crucial component of future research. Because of the many unresolved issues related to data collection and processing, communications, security and privacy, hardware limits and user acceptance, this technology is still in its infancy.…”

Section: Challenges and Concluding Remarksmentioning

confidence: 99%

Review—An Overview on Recent Progress in Screen-Printed Electroanalytical (Bio)Sensors

2022

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Portability is one of the essential keys in the development of modern analytical devices. Screen printing technology is an established technology for both chemical and biosensor development. Screen printing technology has been used to generate a variety of electronic sensors that are rapid, cost-effective, on-site, real-time, inexpensive, and practical for use in healthcare, environmental monitoring, industrial monitoring, and agricultural monitoring. This review aims to describe recent research progress related to the development and improvement of screen-printed electrodes (SPEs). We also demonstrate the wide range of applications, also highlighting the market directions and the need for novel devices to be used by non-specialists. Finally, we conclude and provide an overview of the constraints and future opportunities of SPEs in biosensor application.

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Section: Challenges and Concluding Remarksmentioning

confidence: 99%

Review—An Overview on Recent Progress in Screen-Printed Electroanalytical (Bio)Sensors

2022

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“…Some commonly used metrics for hierarchical clustering include Euclidean distance, Squared Euclidean distance, Manhattan distance, Maximum distance, and Mahalanobis distance. [ 2,53 ] Note the choice of an appropriate metric will influence the hierarchy of the clustering results, as some elements or features may be relatively closer to one another under one metric than another.…”

Section: Algorithms and Augmented Sensing Performancementioning

confidence: 99%

“…

Advances in new materials and soft and stretchable circuits have given rise to the interests in wearable sensing electronic systems (WSESs) for a broad range of applications, including health monitoring, disease diagnosis, personalized healthcare, on-demand treatment, assistive device, human-machine interface (HMI), and virtual and augmented reality. [1][2][3][4][5][6][7][8][9][10][11] Generally, a WSES consists of several heterogenous components: the sensor unit, power unit, wireless communication unit, data collection/storage/ transmission unit, and data processing unit. [12][13][14][15][16] Each of these components is essential to be intelligent and smart, thus enabling the potential large-scale use of WSES.

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mentioning

confidence: 99%

Augmenting Sensor Performance with Machine Learning Towards Smart Wearable Sensing Electronic Systems

Zhang

Suresh

Yang

et al. 2022

Advanced Intelligent Systems

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Wearable sensing electronic systems (WSES) are becoming a fundamental platform to construct smart and intelligent networks for broad applications. Various physiological data are readily collected by the WSES, including biochemical, biopotential, and biophysical signals from human bodies. However, understanding these sensing data, such as feature extractions, recognitions, and classifications, is largely restrained because of the insufficient capacity when using conventional data processing techniques. Recent advances in sensing performance and system‐level operation quality of the WSES are expedited with the assistance of machine learning (ML) algorithms. Here, the state‐of‐the‐art of the ML‐assisted WSES is summarized with emphasis on how the accurate perceptions on physiological signals under different algorithms paradigm augment the performance of the WSES for diverse applications. Concretely, ML algorithms that are frequently implemented in the WSES studies are first synopsized. Then broad applications of ML‐assisted WSES with strengthened functions are discussed in the following sections, including intelligent physiological signals monitoring, disease diagnosis, on‐demand treatments, assistive devices, human–machine interface, and multiple sensations‐based virtual and augmented reality. Finally, challenges confronted for the ML‐assisted WSES are addressed.

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“…By using ML approaches, it is possible to bypass the need for deep understanding of the hidden rules underlying the studied system, but still getting relevant information for the prediction of trends, groups, and characteristics [ 37 , 38 ]. ML models have proved to be robust for the diagnosis of several diseases [ 39 , 40 , 41 , 42 , 43 ], including ophthalmology-related ones [ 4 , 14 ]. In the literature, we can find numerous examples of ML-sensor-array technologies for diagnostics, from techniques to detect lung cancer [ 44 ] to multi-sensors capable of diagnosing respiratory diseases and breast cancer from breath air [ 45 , 46 ], which proves the great potential of these approaches for pre-clinical diagnosis, screening purposes, and to assist practitioners in making fast decisions [ 46 ].…”

Section: Previous Considerationsmentioning

confidence: 99%

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy—Application of Sensor Arrays and Machine Learning

Faura

Boix‐Lemonche

Holmeide

et al. 2022

Sensors

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In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.

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Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Cited by 97 publications

References 199 publications

Review—An Overview on Recent Progress in Screen-Printed Electroanalytical (Bio)Sensors

Review—An Overview on Recent Progress in Screen-Printed Electroanalytical (Bio)Sensors

Augmenting Sensor Performance with Machine Learning Towards Smart Wearable Sensing Electronic Systems

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy—Application of Sensor Arrays and Machine Learning

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