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

Hemamalini, V.; Anand, L.; Nachiyappan, S.; Geeitha, S.; Motupalli, Venkata Ramana; Kumar, Rajagopal; Ahilan, A.; Rajesh, M.

doi:10.1016/j.measurement.2022.111054

Cited by 19 publications

(18 citation statements)

References 50 publications

(49 reference statements)

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“…AI and ML biosensors are applied for detecting hidden disease signatures and for the management of COVID-19 [ 67 , 140 , 148 – 155 ]. It is known that early symptoms of SARS-CoV-2 infection overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners.…”

Section: Application Of Nanobiosensors In Viral Diagnosticsmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Application Of Nanobiosensors In Viral Diagnosticsmentioning

confidence: 99%

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

et al. 2023

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“…Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. A predictive analysis is done in [23] on the downloaded dataset which are designed especially for COVID-19 quarantined patients. The wearable devices used on quarantined patients that provides the healthy and unhealthy patient data.…”

Section: Cognitive Systems a Survey On Cognitive Computing (Cc) Is Do...mentioning

confidence: 99%

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Rahman

Parveen

Sofi

et al. 2023

SCPE

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The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help.

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“…Due to the dependency on physical testing, robust methods involving the use of bio-medical sensors for virus detection evolved. Hemamalini et al employed Multi-Layer Perceptron (MLP) to detect hidden signatures of the virus from the X-ray images [3] . Banerjee et al worked on the X-ray images using Convolution Neural Network (CNN) to reveal the presence of disease [4] .…”

Section: Motivation and Research Objectivesmentioning

confidence: 99%

“…Soon after, on 11 March 2020, it was declared a pandemic [1] . Since then, researchers in all parts of the world have launched intensive efforts to understand the mechanisms of its spread [2] , COVID-19 detection with medical images and biomarkers [3] , [4] , develop and study vaccines against the novel coronavirus [5] , [6] , enforcing safety measures [7] , and analyze the impact of the pandemic [8] – [16] as well as counter-measures taken by governments [17] , [18] , on society.…”

Section: Introductionmentioning

confidence: 99%

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

Chakraverty

Gupta²

2022

Measurement

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

Cited by 19 publications

References 50 publications

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

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

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

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

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