Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning

Gecgel, Ozhan; Ramanujam, A.; Botte, Gerardine G.

doi:10.3390/v14091930

Cited by 6 publications

(4 citation statements)

References 29 publications

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“… The proposed model showed the need for an improved model which may state-of-the-art methods for pandemic detection and prediction. However, the outcome shows the need for further investigation [ 41 ] The dataset was gathered with the help of an incredibly quick COVID-19 diagnostic sensor, and DL methods have been used. Covid-19 dataset CNN NA The findings suggest that SARS-CoV-2 samples may be correctly identified by the CNN algorithm with a 96.15% sensitivity 98.17% specificity and 97.20% accuracy respectively By combining this DL-based model with the already-existing UFC-19, SARS-CoV-2 presence could be better detected.…”

Section: Results and Analysismentioning

confidence: 99%

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Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Ajagbe

Adigun

2023

Multimed Tools Appl

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Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps in the timely detection of any infectious disease (IDs) and is essential to the management of diseases and the prediction of future occurrences. Many scientists and scholars have implemented DL techniques for the detection and prediction of pandemics, IDs and other healthcare-related purposes, these outcomes are with various limitations and research gaps. For the purpose of achieving an accurate, efficient and less complicated DL-based system for the detection and prediction of pandemics, therefore, this study carried out a systematic literature review (SLR) on the detection and prediction of pandemics using DL techniques. The survey is anchored by four objectives and a state-of-the-art review of forty-five papers out of seven hundred and ninety papers retrieved from different scholarly databases was carried out in this study to analyze and evaluate the trend of DL techniques application areas in the detection and prediction of pandemics. This study used various tables and graphs to analyze the extracted related articles from various online scholarly repositories and the analysis showed that DL techniques have a good tool in pandemic detection and prediction. Scopus and Web of Science repositories are given attention in this current because they contain suitable scientific findings in the subject area. Finally, the state-of-the-art review presents forty-four (44) studies of various DL technique performances. The challenges identified from the literature include the low performance of the model due to computational complexities, improper labeling and the absence of a high-quality dataset among others. This survey suggests possible solutions such as the development of improved DL-based techniques or the reduction of the output layer of DL-based architecture for the detection and prediction of pandemic-prone diseases as future considerations.

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Section: Results and Analysismentioning

confidence: 99%

“…The global economy has collapsed due to the coronavirus (COVID-19) pandemic [ 1 , 22 , 41 ]. New strain variants, a lack of social self-control, and optional vaccination all increase the likelihood that COVID-19 will persist and behave like a seasonal sickness.…”

Section: Introductionmentioning

confidence: 99%

Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Ajagbe

Adigun

2023

Multimed Tools Appl

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“…This strategy has the potential to completely transform the field of biosensing and showed considerable promise in terms of enhancing the precision and dependability of biosensors, notably in the context of non-invasive SARS-CoV-2 testing. Specific examples will be given in Section 4 of using supervised algorithms such as SVMs to improve SARS-CoV-2 biosensors by recognizing specific patterns in a dataset and assigning the data to the correct class [ 37 , 38 ].…”

Section: Machine Learning-enhanced Biosensors For Non-invasive Samplingmentioning

confidence: 99%

“…The highest achieved accuracy was 97.2%, which outperformed the DTC algorithm. They suggested that combining this CNN model with UFC-19 could enable selective identification of SARS-CoV-2 presence within two minutes [ 38 ].…”

Section: Applications Of Machine Learning-enhanced Biosensors For Sar...mentioning

confidence: 99%

Advancements in SARS-CoV-2 Testing: Enhancing Accessibility through Machine Learning-Enhanced Biosensors

Georgas,

Hristoforou

2023

Micromachines

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The COVID-19 pandemic highlighted the importance of widespread testing for SARS-CoV-2, leading to the development of various new testing methods. However, traditional invasive sampling methods can be uncomfortable and even painful, creating barriers to testing accessibility. In this article, we explore how machine learning-enhanced biosensors can enable non-invasive sampling for SARS-CoV-2 testing, revolutionizing the way we detect and monitor the virus. By detecting and measuring specific biomarkers in body fluids or other samples, these biosensors can provide accurate and accessible testing options that do not require invasive procedures. We provide examples of how these biosensors can be used for non-invasive SARS-CoV-2 testing, such as saliva-based testing. We also discuss the potential impact of non-invasive testing on accessibility and accuracy of testing. Finally, we discuss potential limitations or biases associated with the machine learning algorithms used to improve the biosensors and explore future directions in the field of machine learning-enhanced biosensors for SARS-CoV-2 testing, considering their potential impact on global healthcare and disease control.

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Detection of SARS-CoV-2 based on artificial intelligence-assisted smartphone: A review

Li,

Zhang,

Chen

et al. 2024

Chinese Chemical Letters

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Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning

Cited by 6 publications

References 29 publications

Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Advancements in SARS-CoV-2 Testing: Enhancing Accessibility through Machine Learning-Enhanced Biosensors

Detection of SARS-CoV-2 based on artificial intelligence-assisted smartphone: A review

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