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Arya SS, Dias SB, Jelinek HF, Hadjileontiadis LJ, Pappa AM. The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics? Biosens Bioelectron 2023; 235:115387. [PMID: 37229842 DOI: 10.1016/j.bios.2023.115387] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/11/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023]
Abstract
Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.
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Affiliation(s)
- Sagar S Arya
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Interdisciplinary Center for Human Performance, Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal.
| | - Herbert F Jelinek
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR, 54124, Thessaloniki, Greece
| | - Anna-Maria Pappa
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates; Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge, UK.
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Giordano GF, Ferreira LF, Bezerra ÍRS, Barbosa JA, Costa JNY, Pimentel GJC, Lima RS. Machine learning toward high-performance electrochemical sensors. Anal Bioanal Chem 2023:10.1007/s00216-023-04514-z. [PMID: 36637495 PMCID: PMC9838410 DOI: 10.1007/s00216-023-04514-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 01/14/2023]
Abstract
The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.
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Affiliation(s)
- Gabriela F. Giordano
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil
| | - Larissa F. Ferreira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970 Brazil
| | - Ítalo R. S. Bezerra
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil
| | - Júlia A. Barbosa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590 Brazil
| | - Juliana N. Y. Costa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil
| | - Gabriel J. C. Pimentel
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,School of Sciences, São Paulo State University, Bauru, São Paulo 17033-360 Brazil
| | - Renato S. Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil ,São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590 Brazil
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Puthongkham P, Wirojsaengthong S, Suea-Ngam A. Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry. Analyst 2021; 146:6351-6364. [PMID: 34585185 DOI: 10.1039/d1an01148k] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.
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Affiliation(s)
- Pumidech Puthongkham
- Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand. .,Electrochemistry and Optical Spectroscopy Center of Excellence (EOSCE), Chulalongkorn University, Bangkok 10330, Thailand.,Center of Excellence in Responsive Wearable Materials, Chulalongkorn University, Bangkok 10330, Thailand
| | - Supacha Wirojsaengthong
- Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
| | - Akkapol Suea-Ngam
- Department of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK
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Aiassa S, Ros PM, Hanitra MIN, Tunzi D, Martina M, Carrara S, Demarchi D. Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:294-302. [PMID: 33739925 DOI: 10.1109/tbcas.2021.3067388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved with electrochemical techniques, and several options are present in literature to measure propofol (widely used anaesthetics). Still, the sensors proposed do not enable in-situ detection, they do not provide this information continuously, and they are based on bulky and costly lab equipment. In this paper, we present a novel smart pen-shaped electronic system for continuous monitoring of propofol in human serum. The system consists of a needle-shaped sensor, a quasi digital front-end, a smart machine learning data processing, in a single wireless battery-operated embedded device featuring Bluetooth Low Energy (BLE) communication. The system has been tested and characterized in real, undiluted human serum, at 37 °C. The device features a limit of detection of 3.8 μM, meeting the requirement of the target application, with an electronics system 59% smaller and 81% less power consuming w.r.t. the state-of-the-art, using a smart machine learning classification for data processing, which guarantees up to twenty continuous measure.
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