RETRACTED ARTICLE: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods
Open accessAbstract
Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.
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AI techniques, with a deep learning model including RNN and CNN algorithms, could predict and diagnose coronavirus using an Oura smart ring rapidly within 24h.
“Recently, AI techniques, with the help of a deep learning model including Recurrent neural networks (RNN) and convolutional neural network (CNN) algorithms, could predict and diagnose coronavirus using an Oura smart ring rapidly within 24h [218].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [218]
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- Authors
- Poongodi, M., Hamdi, M., Malviya, M., Sharma, A., Dhiman, G., & Vimal, S.
- Affiliations
- Hamad bin Khalifa University (QA)
- Wipro (India) (IN)
- Southern Federal University (RU)
- Punjabi University (IN)
- Corresponding author
- Dhiman, Gaurav
- Venue
- Personal and Ubiquitous Computing
- Published
- 2021-02-26
- Volume
- 26
- Issue
- 1
- Pages
- 25-35
- ISSN
- 1617-4909 (ISSN-L)
- 1617-4917
- PMC
- PMC7908947
- OpenAlex
- W3135672363
- Cited by
- 65
- References
- 24
- Citations by year
- 2025: 7 · 2024: 6 · 2023: 22 · 2022: 18 · 2021: 12
- FWCI
- 7.65
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- Open access
- bronze
- doi.org
- Licence
- Locations
- Personal and Ubiquitous Computing (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 diagnosis, wearable sensors, hybrid deep learning model, recurrent neural networks, convolutional neural networks, chest X-ray classification, CT image classification, symptom severity prediction, rapid diagnostic tests, false positive and false negative reduction
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