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使用深度学习方法通过可穿戴的奥ura智能戒指诊断和对抗新冠病毒。

Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

作者信息

Poongodi M, Hamdi Mounir, Malviya Mohit, Sharma Ashutosh, Dhiman Gaurav, Vimal S

机构信息

College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Department of CTO 5G, Wipro Limited, Bengaluru, India.

出版信息

Pers Ubiquitous Comput. 2022;26(1):25-35. doi: 10.1007/s00779-021-01541-4. Epub 2021 Feb 26.

DOI:10.1007/s00779-021-01541-4
PMID:33654480
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7908947/
Abstract

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.

摘要

自冠状病毒（COVID-19）疫情在全球持续蔓延以来，科学家们一直在研发各种技术，主要聚焦于人工智能，以应对这一疫情带来的挑战。在当前这场全球紧急情况中，临床行业正在寻求新的进展来筛查和对抗COVID-19感染。人工智能所采用的策略能够广泛监测感染的传播，识别高感染风险患者，并在持续监测病情方面发挥有效作用。人工智能预测还可通过充分分析过往患者的数据来传递风险。为人群检测、医疗护理、通报和感染控制提供建议的国际患者支持有助于对抗这种致命病毒。我们提出了一种混合深度学习方法来诊断COVID-19。这里采用分层方法来衡量患者的症状水平，并分析患者的图像数据，以判断其是否感染COVID-19呈阳性。这项工作利用智能人工智能技术，借助欧若智能手环在24小时内快速预测和诊断冠状病毒。在实验室中，借助深度学习模型，利用循环神经网络（RNN）和卷积神经网络（CNN）算法，制备了一种冠状病毒快速检测方法，以快速、准确地诊断冠状病毒。结果显示为0或1。结果1表示该人感染了冠状病毒，结果0表示该人未感染冠状病毒。这里考虑了X射线和CT图像分类，以便利用阈值从初始阶段到严重阶段识别个体的健康状况。阈值0.5用于识别冠状病毒初始阶段状况，1用于识别患者的冠状病毒严重状况。所提出的方法用于四个加权参数，以减少COVID-19快速准确诊断中假阳性和假阴性图像分类结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/61efe25e51b0/779_2021_1541_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/b9d31cc80e07/779_2021_1541_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/5d1a8ed191a3/779_2021_1541_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/3777ea3f74eb/779_2021_1541_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/15c88841300a/779_2021_1541_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/998907fbe43c/779_2021_1541_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/ded2cf9a3a39/779_2021_1541_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/2f6a23cf4c4c/779_2021_1541_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/be4babd677c2/779_2021_1541_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/61efe25e51b0/779_2021_1541_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/b9d31cc80e07/779_2021_1541_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/5d1a8ed191a3/779_2021_1541_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/3777ea3f74eb/779_2021_1541_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/15c88841300a/779_2021_1541_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/998907fbe43c/779_2021_1541_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/ded2cf9a3a39/779_2021_1541_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/2f6a23cf4c4c/779_2021_1541_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/be4babd677c2/779_2021_1541_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4a5/7908947/61efe25e51b0/779_2021_1541_Fig9_HTML.jpg

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引用本文的文献

[1]
[Wearable devices: Perspectives on assessing and monitoring human physiological status].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023-12-25

[2]
The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review.

Sensors (Basel). 2023-11-29

[3]
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Open Res Eur. 2022-8-22

[4]
Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization.

Diagnostics (Basel). 2023-9-12

[5]
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PLOS Digit Health. 2022-9-6

[6]
A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms.

Front Physiol. 2023-1-30

[7]
Machine learning and deep learning approach for medical image analysis: diagnosis to detection.

Multimed Tools Appl. 2022-12-24

[8]
To lockdown or not to lockdown: Analysis of the EU lockdown performance vs. COVID-19 outbreak.

Front Med Technol. 2022-10-21

[9]
A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment.

J Pers Med. 2022-10-5

[10]
An AI-enabled pre-trained model-based Covid detection model using chest X-ray images.

Multimed Tools Appl. 2022

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