Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic

Boddu, Raja Sarath Kumar; Karmakar, Partha; Bhaumik, Ankan; Nassa, Vinay Kumar; Vandana, .; Bhattacharya, Sumanta

doi:10.1016/j.matpr.2021.11.549

Cited by 14 publications

(6 citation statements)

References 17 publications

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“…Finally, it should be recalled that almost all research questions (hypotheses) related to the development of a smart solution to address and prevent the risk of virus spread have been answered. In addition, comparing it to other works [36][37][38][39], we conclude that this solution has strong additions, whether in response (through results) or adaptation (ontology-based solution) to all developments. Accordingly, many recommendations regarding investment in this solution can be made by researchers specialized in the field of medicine.…”

Section: Analyze and Discussionmentioning

confidence: 74%

Development of an Ontology-Based Solution to Reduce the Spread of Viruses

et al. 2022

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With the sudden emergence of many dangerous viruses in recent years and with their rapid transmission and danger to individuals, most countries have adopted several strategies, such as closure and social distancing, to control the spread of the virus in the population. In parallel with all these precautions, scientific laboratories are working on developing the appropriate vaccine, which in many cases takes many years. Until then, it is necessary to resort to many solutions, including solutions that rely on information technologies and artificial intelligence (AI). In this context, this paper proposes a new solution based on the ontology and rules of intelligent reasoning. Initially, the virus environment is analyzed, followed by the extraction and editing of the main elements of the ontology using the “Protégé” software. In the last step, the proposed solution is tested, by choosing the city of Adrar in southwestern Algeria, which was particularly affected by COVID-19. Three scenarios were shown for different cases. The efficiency of the proposed solution was confirmed through the instructions it provides in the event of symptoms appearing in a person. In addition, this solution helps the competent authorities know the location and extent of the epidemic by informing the local communities.

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Section: Analyze and Discussionmentioning

confidence: 74%

Development of an Ontology-Based Solution to Reduce the Spread of Viruses

et al. 2022

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“…Application of AI tools, namely DL architectures, to medical imaging, specifically CT scans, which are the ones more often done on patients with pneumonia complications, have been shown to be useful for the diagnosis and prognosis of diverse lung affections [175][176][177]. This approach also has value as a preliminary screening tool aiming to diminish the workload on hospital staff and reduce the rate of misdiagnosis of patients with COVID-19 [178][179][180].…”

Section: Mortality Predictionmentioning

confidence: 99%

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.

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“…Finally, artificial intelligence or machine learning methods, commonly used in different domains, were also applied during the pandemic to improve data analysis and the development of some prognostic or predictive scores. 4 , 10 , 11 These methods also have huge potential to aid data collection, although in reality natural language processing remains challenging for practical implementation. 12 In the context of clinical prediction efforts, particular emphasis should be placed on the reliability, robustness and fairness of the proposed models as well as its transparency, utility and acceptability for health care professionals.…”

Section: Rapid Organization Of Rwe Infrastructures For Data Collectio...mentioning

confidence: 99%

Learning lessons from the COVID-19 pandemic for real-world evidence research in oncology—shared perspectives from international consortia

et al. 2023

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Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic

Cited by 14 publications

References 17 publications

Development of an Ontology-Based Solution to Reduce the Spread of Viruses

Development of an Ontology-Based Solution to Reduce the Spread of Viruses

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Learning lessons from the COVID-19 pandemic for real-world evidence research in oncology—shared perspectives from international consortia

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