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1.
AIMS Biophysics ; 8(4):346-371, 2021.
Article in English | Scopus | ID: covidwho-1964164

ABSTRACT

The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system’s accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients © 2021. the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

2.
Enabling Healthcare 4.0 for Pandemics: A Roadmap Using AI, Machine Learning, IoT and Cognitive Technologies ; : 237-250, 2021.
Article in English | Scopus | ID: covidwho-1919213

ABSTRACT

The whole world at present is under the grasp of a pandemic termed as COVID-19. The World Health Organization (WHO) guidelines suggest that the social distancing norms are followed with contactless operations as far as possible. Therefore, the population around the world is turning towards efficient modes of operating the daily work with minimal human contact. To contain the spread of the novel coronavirus or COVID-19, it is important and suitable to deploy machinery for operating in conditions wherever social distancing is required. The multipurpose robot makes it feasible to minimize human contact and carry out operations without the risk of the spread of the virus. This chapter aims at the fabrication of a robot that can have multiple utilities and is employed in different areas as per the requirement of the user. © 2021 Scrivener Publishing LLC.

3.
International Journal of Statistics in Medical Research ; 10:146-160, 2021.
Article in English | Scopus | ID: covidwho-1591784

ABSTRACT

Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem. © 2021 Lifescience Global. All Rights Reserved.

4.
1st International Conference on Advances in Medical Physics and Healthcare Engineering, AMPHE 2020 ; : 393-404, 2021.
Article in English | Scopus | ID: covidwho-1353686

ABSTRACT

The entire world faced locked down scenario due to the outbreak of nCOVID-19 corona virus outbreak. The fast and relentless spread nCOVID-19 has basically segmented the populace only into three subclasses, namely susceptible, infected, and recovered compartments. Adapting the classical SIR-type epidemic modeling framework, the direct person-to-person contact transmission is taken as the direct route of transmission of nCOVID-19 pandemic. In this research, the authors have developed two models of the nation-wide trends of the outburst of the nCOVID-19 infection using an SIR model and also an ARIMA model. They have studied the quantile plots, regression residual plots and R pair plots of the dataset by simple supervised machine learning algorithms. This study compares both models and higher correlation of the developed models with reality which suggests the extent of accuracy of these models. The study also suggested some possible way-out to get rid of this situation by providing a trade-off between ‘flattening of the curve’ as well as less economic turbulence. The projections are intended to provide an action plan for the socioeconomic counter measures to alleviate COVID-19 in India. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

5.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-6248

ABSTRACT

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

6.
Current Science ; 119(9):1489-1498, 2020.
Article in English | Scopus | ID: covidwho-967247

ABSTRACT

Novel coronavirus (SARS-CoV-2), a variant of the severe acute respiratory syndrome (SARS) family has claimed around 1 million lives and more than 33 million people worldwide have been infected. It has been declared a pandemic by the World Health Organization. COVID-19 is transmitted mainly through aerosol droplets from patients (both asymptomatic and symptomatic) to healthy people. Its high rate of transmission demands a quick and early diagnosis of patients followed by urgent quarantine of those affected. Since the SARS-CoV-2 virus is mutating, it is of utmost importance to develop a quick diagnosis against it. The current techniques use either PCR-based methods or antibody-based ELISA methods for diagnosis, which are both time-consuming and expensive. This is the biggest impediment in large-scale diagnosis of COVID-19. Multiple biosensors based on antibodies and aptamers have been reported and tested. Aptamers seem much more lucrative due to ease of synthesis, cost-effectiveness and extremely high degree of sensitivity in terms of detection, less immunogenicity and robustness to modifications. We present the history and characterization of aptamers, their selection strategies and applications to multiple viruses such as HIV, HCV and SARS-CoV. However, to date, no aptamers have been designed against any of the protein components or the genomic RNA of SARS-CoV-2. Based on the success of aptamers against many viruses, we argue for the future exploration of aptamers in the context of SARS-CoV-2 diagnostic testing. © 2020. All Rights Reserved.

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