Chủ đề : AI For COVID-19

1. Artificial intelligence for COVID-19 - INTRODUCE

As 2020 draws to a close, one thing is certain: the COVID-19 pandemic has had an irreversible effect on
the world. The effect on digital health is no exception. The pandemic has forced health-care providers
and governments around the world to accelerate the development of artificial intelligence (AI) tools and
scale up their use in medicine, even before they are proven to work. An untested AI algorithm has even
received emergency authorisation from the US Food and Drug Administration. But will the use of
untested AI systems help or hinder patients with COVID-19?

Since first reported in Wuhan, China, in late December 2019,the outbreak of the novel coronavirus now
known as SARS-CoV-2 has spread globally. As of the second week of August2020, the novel coronavirus
disease (COVID-19) pandemichas now reached over 21 million confirmed cases worldwideand
approximately 762,000 deaths worldwide according tothe Johns Hopkins University Coronavirus
Resource Cen-ter. COVID-19 case numbers are alarming in both their vol-ume and widening geographic
scope but there are also con-cerns about the accuracy of reported COVID-19 case num-bers, particularly
at earlier stages of the pandemic and in thelast months within the Latin American region , and
whetherunder-reporting may have blurred the true extent of the out-break from the beginning and its
health and economical im-pact. There are many studies using Google Trends to ana-lyze, forecast topics
related to dengue (Anggraeni and Aris-tiani 2016), elections (Polykalas, Prezerakos, and Konidaris2013),
programming (Chen and Xing 2016) and recently,studying the COVID-19 topic in Ghana (Chire and
Panford-Quainoo 2020), France (Chire and Oblitas Cruz 2020) , Cen-
(PDF) Analysis of internet trends related to medications for COVID-19 in ten countries with the highest
number of cases. [1]

To support transparent and reproducible reporting, source code and deidentified patient datasets for
COVID-19 AI algorithms should be open and accessible to the research community. One such study,
published in The Lancet Digital Health, reports a new AI COVID-19 screening test, named CURIAL AI,
which uses routinely collected clinical data for patients presenting to hospital. In the hope that AI can
help keep patients and health-care workers safe, Andrew Soltan and colleagues state that the AI test
could allow exclusion of patients who do not have COVID-19 and ensure that patients with COVID-19
receive treatments rapidly. This is one of the largest AI studies to date with clinical data from more than
a hundred thousand cases in the UK. Prospective validation of the AI screening test showed accurate
and faster results compared with gold standard PCR tests.[2]

2. Main applications of AI in COVID-19 pandemic

AI can quickly analyze irregular symptom and other‘red flags’and thus alarm the patients and the health
From the emergence of Covid-19, the primary focus of governments across the world is contact
tracing of the effected person. To achieve this, most of the governments use smartphone data to
track where people go and with whom they interact. In this regard, Government of India is also using
‘Arogya Setu App’ successfully. These so-called contact-tracing apps help public health officials get
ahead of the spread of Covid-19. However, the downside is the inherent loss of privacy. If abused,
raw location data of a person could reveal sensitive information related to that person using the app.
Therefore, this work proposes to develop a lightweight cryptographic algorithm such that sensitive
location data can be securely transferred to the destination without being compromised. care
authorities . It helps to provide faster decision making, which is cost-effective. It helps to develop a new
diagnosis and management system for the COVID 19 cases, through useful algorithms. AI is helpful in the
diagnosis of the infected cases with the help of medical imaging technologies like Computed
tomography (CT), Magnetic resonance imaging (MRI) scan of human body parts

AI can build an intelligent platform for automatic monitoring and prediction of the spread of this virus. A
neural network can also be developed to extract the visual features of this disease, and this would help
in proper monitoring and treatment of the affected individuals . It has the capability of providing day-to-
day updates of the patients and also to provide solutions to be fol-lowed in COVID-19 pandemic. His
technology can track and forecast the nature of the virus from the available data, social media and
media platforms, about the risks of the infection and its likely spread. Further, it can predict the number
of positive cases and death in any region. AI can help identify the most vulnerable regions, people and
countries and take measures accordingly. With the help of real-time data analysis, AI can provide
updated information which is helpful in the prevention of this disease. It can be used to predict the
probable sites of infection, the influx of the virus, need for beds and healthcare professionals during this
crisis.AI is helpful for the future virus and diseases prevention, with the help of previous mentored data
over data prevalent at different time.[3]

The data processing addressed by the present DPIA is related to the global coronapandemic, which
began in Wuhan, People’s Republic of China, at the end of 2019and has since spread throughout the
world. After the first appearance of infectionsin Europe at the end of January 2020 – first in France on
January 24, four dayslater in Germany – it took until mid-March – about 40 days – until
comprehensivemeasures were taken in Germany, both preparatory and containment measures. On22
March 2020, the Federal Government and the Länder agreed on a “comprehensiverestraining order”,
and the Länder of Bavaria, Berlin, Brandenburg, Saarland, Saxonyand Saxony-Anhalt agreed on an even
further reaching curfew. As of 7 April 2020, themeasures are to be maintained until at least 19 April
2020. [4]

The MCST App supports recovery of small businesses by providing information on COVID-19 related
aspects such as safety measures. It caters to vendors (owners) & patrons (customers) entailing
businesses such as restaurants, grocery stores, and pharmacies. The app provides user profile options
for vendors and search options for patrons, along with simple browsing, calling, emailing, locations,
pictures of the concerned site (restaurant etc.), descriptions, COVID-19 precautions and more. It goes
beyond “yellow pages” since it is more interactive. Yellow pages are static while MSCT has user-
generated content and is dynamic. Using MCST during COVID-19 recovery, vendors can populate
additional fields and add new entries speedily, e.g. “all employees are COVID-19 vaccinated”. Such
timely information could be highly relevant to safety precautions.[5]

The densely connected convolutional network (DenseNet) has a convolutional neural network
architecture that is state-of-the-art according to the classification results with the ImageNet
validation dataset. Huang et al. [9] used a direct connection from each layer to every other layer in a
feed-forward direction. Each layer in the network receives concatenation of the feature maps
produced in the previous layers as inputs and implements nonlinear functions such as batch
normalization, ReLU, and convolution or pooling. After the nonlinear function operation, the
product feature maps of each layer used as inputs to every after layers. The concatenation
operation is not effective when the size of the feature maps varies, thus the pooling operation is
important by changing the size of the feature maps.[6]

The unprecedented and large number of patients, especially in the regions where the severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has hit badly, has created an immediate
need for novel approaches to dealing with the issue.  One such approach is the application of artificial
intelligence (AI). Now, in a new research paper published on the medRxiv* server, an international team
of scientists has systematically reviewed and critically appraised the current evidence on AI applications
for COVID-19 in intensive care and emergency settings, focusing on methods, reporting standards, and
clinical utility.

This Viewpoint provides a framework for the application of digital technologies in pandemic
management and response, highlighting ways in which successful countries have adopted these
technologies for pandemic planning, surveillance, testing, contact tracing, quarantine, and health care.
[7]

MERS-CoV has also been classified as a new virus of the group II CoVs (Beta-CoVs) in the Coronaviridae
family.63 In order to enter host cells, MERS-CoV, similar to SARS-CoV, requires a receptor for the S
protein. In the case of MERS-CoV, candidate proteins that act as receptors have been identified as
tetopeptidases, such as dipeptidyl peptidase 4 (DPP4)[8]

Contact Tracing App concept and implementation to connect government, commercial institutions and
people.

Our approach to integrating contact tracing, possible isolation requirements ( post-travel or after a
positive test) and social lockdown measures (mobility).[9]

From the emergence of Covid-19, the primary focus of governments across the world is contact tracing
of the effected person. To achieve this, most of the governments use smartphone data to track where
people go and with whom they interact. In this regard, Government of India is also using ‘Arogya Setu
App’ successfully. These so-called contact-tracing apps help public health officials get ahead of the
spread of Covid-19. However, the downside is the inherent loss of privacy. If abused, raw location data
of a person could reveal sensitive information related to that person using the app. Therefore, this work
proposes to develop a lightweight cryptographic algorithm such that sensitive location data can be
securely transferred to the destination without being compromised.[10]
 Tài liệu thao khảo :
[1] J. Chire and R. Lemus, Analysis of internet trends related to medications for COVID-19 in ten
countries with the highest number of cases. 2020.

[2] T. L. D. Health, “Artificial intelligence for COVID-19: saviour or saboteur?,” The Lancet Digital
Health, vol. 3, no. 1, p. e1, Jan. 2021, doi: 10.1016/S2589-7500(20)30295-8.

[3] R. Vaishya, M. Javaid, I. Haleem Khan, and A. Haleem, “Artificial Intelligence (AI) applications for
COVID-19 pandemic,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 14, Apr. 2020,
doi: 10.1016/j.dsx.2020.04.012.

[4] K. Bock, C. Kühne, R. Mühlhoff, M. Ost, J. Pohle, and R. Rehak, “Data Protection Impact
Assessment for the Corona App,” SSRN Electronic Journal, Jan. 2020, doi: 10.2139/ssrn.3588172.

[5] J. Torres, C. Duran, V. Anu, and A. Varde, “MCST App: My-Covid-Safe-Town.” Mar. 31, 2021, doi:
10.13140/RG.2.2.12191.48802/2.

[6] S. Vasal, “COVID-AI: An Artificial Intelligence System to Diagnose COVID-19 Disease,”

[7] S. Whitelaw, M. A. Mamas, E. Topol, and H. G. C. Van Spall, “Applications of digital technology in
COVID-19 pandemic planning and response,” Lancet Digit Health, vol. 2, no. 8, pp. e435–e440, Aug.
2020, doi: 10.1016/S2589-7500(20)30142-4.

[8] D. Yang, “<p>Application of Nanotechnology in the COVID-19 Pandemic</p>,” IJN, vol. 16, pp.
623–649, Jan. 2021, doi: 10.2147/IJN.S296383.

[9] “COVID-19 Contact Tracing App - Australia,” ResearchGate.

[10] “Toward Securing Contact-tracing apps related to Covid-19,” ResearchGate.