Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence

Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

Label-Free Field Effect Transistors (FETs) for Fabrication of Point-of-Care (POC) Biomedical Detection Probes

Field effect transistors (FETs)-based detection probes are powerful platforms for quantification in biological media due to their sensitivity, ease of miniaturization, and ability to function in biological media. Especially, FET-based platforms have been utilized as promising probes for label-free detections with the potential for use in real-time monitoring. The integration of new materials in the FET-based probe enhances the analytical performance of the developed probes by increasing the active surface area, rejecting interfering agents, and providing the possibility for surface modification. Furthermore, the use of new materials eliminates the need for traditional labeling techniques, providing rapid and cost-effective detection of biological analytes. This review discusses the application of materials in the development of FET-based label-free systems for point-of-care (POC) analysis of different biomedical analytes from 2018 to 2024. The mechanism of action of the reported probes is discussed, as well as their pros and cons were also investigated. Also, the possible challenges and potential for the fabrication of commercial devices or methods for use in clinics were discussed.

TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm

BACKGROUND

Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities.

OBJECTIVE

Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms.

METHODS

The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data.

RESULTS

According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities.

CONCLUSION

From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.

Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses

Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.

Carbon-based biosensors: Next-generation diagnostic tool for target-specific detection of SARS-CoV-2 (COVID-19)

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was declared a global pandemic in 2020. Having rapidly spread around the globe, with the emergence of new variants, there is a crucial need to develop diagnostic kits for its rapid detection. Since it validated accuracy and reliability, the reverse transcription polymerase chain reaction (RT-PCR) test has been declared the gold standard for disease detection. However, despite its reliability, the requirement of specialized facilities, reagents, and duration of a PCR run limits its usage for rapid detection. There is thus a continuous increase in the design and development of rapid, point-of-care (PoC), and cost-effective diagnostic kits. In this review, we discuss the potential of carbon-based biosensors for target-specific detection of coronavirus disease 19 (COVID-19) and present an overview of investigation within the timeframe of the last four years (2019-2022), which have developed novel platforms using carbon nanomaterial-based approaches for viral detection. The approaches discussed offer rapid, accurate, and cost-effective strategies for COVID-19 detection for healthcare personnel and research workers.

A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique

COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.

As a pandemic strikes: A study on the impact of mental stress, emotion drifts and activities on community emotional well-being

The widespread, ongoing COVID-19 pandemic has brought to the fore concerns regarding the psychological well-being of people. Recent research revealed various issues impacting mental health of people. However, a systematic study of the emotional drift of the populace, has been precluded so far. Our investigative research seeks to explore stress factors for different subgroups in India, variation in primary emotions during COVID-19 initial phase, and the emotional impact of activities practiced by people to adjust to the new norms. We conduct an online questionnaire-based survey that elicits responses from 958 participants. Our analysis establishes significant correlations between pandemic-induced causative factors and stresses in subgroups and micro-community. Unexpected events during the pandemic disturbed community's emotional equilibrium. Lastly, we find specific activities demonstrating an ameliorative impact on the emotional well-being of people. Our analysis emphasizes the need for a pre-planned infrastructure to provide Psychological First Aid (PFA) to foster psychological preparedness.