Analyzing chronic disease biomarkers using electrochemical sensors and artificial neural networks

Sinha, Kshitij; Uddin, Zakir; Kawsar, H.I.; Islam, Shofiqul; Deen, M. Jamal; Howlader, Matiar R.

doi:10.1016/j.trac.2022.116861

Cited by 20 publications

(14 citation statements)

References 153 publications

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“…The data were interpreted using Matlab [ 45 ]. The utilization of machine learning and artificial intelligence (AI) is proposed to carry out more accurate data analysis [ 48 ].…”

Section: Recent Advancesmentioning

confidence: 99%

“…As previously mentioned, 2D nanomaterials have a high specific surface area [ 15 ], great mechanical stiffness [ 48 ], good functionalization ability [ 13 ], high electrical conductivity [ 15 ], and a strong electrocatalytic effect [ 13 ]. Currently, nanocomposites made of graphene/MWCNTs as the matrix and metal oxide NPs or conductive polymers as the second component are being used for direct electrochemical detection of cannabinoids.…”

Section: Recent Advancesmentioning

confidence: 99%

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Nanomaterials-Based Electrochemical Δ9-THC and CBD Sensors for Chronic Pain

2023

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Chronic pain is now included in the designation of chronic diseases, such as cancer, diabetes, and cardiovascular disease, which can impair quality of life and are major causes of death and disability worldwide. Pain can be treated using cannabinoids such as Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD) due to their wide range of therapeutic benefits, particularly as sedatives, analgesics, neuroprotective agents, or anti-cancer medicines. While little is known about the pharmacokinetics of these compounds, there is increasing interest in the scientific understanding of the benefits and clinical applications of cannabinoids. In this review, we study the use of nanomaterial-based electrochemical sensing for detecting Δ9-THC and CBD. We investigate how nanomaterials can be functionalized to obtain highly sensitive and selective electrochemical sensors for detecting Δ9-THC and CBD. Additionally, we discuss the impacts of sensor pretreatment at fixed potentials and physiochemical parameters of the sensing medium, such as pH, on the electrochemical performance of Δ9-THC and CBD sensors. We believe this review will serve as a guideline for developing Δ9-THC and CBD electrochemical sensors for point-of-care applications.

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“…The data were interpreted using Matlab [ 45 ]. The utilization of machine learning and artificial intelligence (AI) is proposed to carry out more accurate data analysis [ 48 ].…”

Section: Recent Advancesmentioning

confidence: 99%

Section: Recent Advancesmentioning

confidence: 99%

Nanomaterials-Based Electrochemical Δ9-THC and CBD Sensors for Chronic Pain

2023

Self Cite

View full text Add to dashboard Cite

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“…Given the rise of personalized healthcare monitoring systems equipped with integrated simultaneous electrochemical biosensors, and non-invasive wearable devices with wireless data communications, it is clear that there is a need to handle massive and complex data with sophisticated analytical tools. [234,[259][260][261][262][263] These systems can generate a wide range of sensing data using various biofluids, which exceeds the human capacity for processing. [259] This necessitates an analytical technique that can provide easy, fast, and accurate results for disease conditions, as well as predictions of health conditions.…”

Section: Analyzing Large Data Using Machine Learningmentioning

confidence: 99%

Analyzing Electrochemical Sensing Fundamentals for Health Applications

Alam,

Mitea,

Howlader

et al. 2023

Advanced Sensor Research

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Humans continuously interact with physical, chemical, and biological environments that influence their health, safety, and quality of life. Sensing devices, such as electrochemical sensors that translate environmental qualities into electrical signals, are crucial for detecting biomarker concentrations in various biofluids. However, the understanding of electrochemical sensing is often incomplete, necessitating further study of chemical reactions and sensor‐electrode interactions for healthcare applications. This review analyzes crucial topics in chemical reactions in electrochemical sensing environments. First, the dynamics of chemical energy, the roles of acidic and alkaline fluids, chemical reaction tendencies, thermodynamic equilibria, Gibbs free energy, water dissociation, and the pH scale are discussed. Sensor materials or biomarkers undergo oxidation and reduction reactions in electrochemical sensing. Oxygen‐derived radicals and nonradical reactive species significantly influence biochemical reactions, cellular responses, and clinical outcomes. Then, the review delves into the impact of oxidation reduction reactions on human pathophysiology, redox reactions in hemoglobin, redox environments in human serum albumin and cells/tissues, and thermodynamics of biological redox reactions. Finally, recent advances in electrochemical techniques are presented and research challenges and future perspectives in electrochemical sensing for health applications are addressed.

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“…The large amount of data obtained from sensing devices analyzed by AI algorithms can lead to more opportunities for proactive, modernized, and personalized medicine. [31][32][33] For example, Juang et al used LSTM and RNN algorithms to analyze and learn the patient's historical medical data and clinician's decision, so that the proposed clinical decision support system (CDSS) can evaluate the patient's daily health status and provide comprehensive medical care recommendations. [34] AI algorithms can improve the sensitivity from 26.44% to 80.84%, meanwhile, yield 99.65% accuracy and 99.95% specificity.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Chen

Cui

Haick

et al. 2023

Advanced Sensor Research

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The aging population and the prevalence of infectious diseases have impacted the traditional medical order, significantly increasing the burden on healthcare and adversely affecting the socio‐economic system. Medical sensors based on artificial intelligence (AI) provide new ideas for modern medical data collection to monitor the health status of individuals and environmental changes. Meanwhile, with the aid of AI algorithms, the big data processing capabilities of sensor systems have been greatly improved, further realizing early predictions and timely diagnoses. In this paper, a brief overview is offered on the development status of AI‐enabled medical sensors for off‐body detection, near‐body monitoring, disease prediction, and clinical decision support system, and the ongoing challenges and future prospects to move from concept to implementation are discussed. In the foreseeable future, breakthroughs in the combination of medical sensors and AI algorithms are expected to pave the way for early detection and clinical decision support and improve the accuracy and efficiency for disease diagnosis.

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Analyzing chronic disease biomarkers using electrochemical sensors and artificial neural networks

Cited by 20 publications

References 153 publications

Nanomaterials-Based Electrochemical Δ9-THC and CBD Sensors for Chronic Pain

Nanomaterials-Based Electrochemical Δ9-THC and CBD Sensors for Chronic Pain

Analyzing Electrochemical Sensing Fundamentals for Health Applications

Artificial Intelligence‐Based Medical Sensors for Healthcare System

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