@article{3070814,
    title = "A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence",
    author = "Suri, J.S. and Agarwal, S. and Gupta, S.K. and Puvvula, A. and Biswas, M. and Saba, L. and Bit, A. and Tandel, G.S. and Agarwal, M. and Patrick, A. and Faa, G. and Singh, I.M. and Oberleitner, R. and Turk, M. and Chadha, P.S. and Johri, A.M. and Miguel Sanches, J. and Khanna, N.N. and Viskovic, K. and Mavrogeni, S. and Laird, J.R. and Pareek, G. and Miner, M. and Sobel, D.W. and Balestrieri, A. and Sfikakis, P.P. and Tsoulfas, G. and Protogerou, A. and Misra, D.P. and Agarwal, V. and Kitas, G.D. and Ahluwalia, P. and Teji, J. and Al-Maini, M. and Dhanjil, S.K. and Sockalingam, M. and Saxena, A. and Nicolaides, A. and Sharma, A. and Rathore, V. and Ajuluchukwu, J.N.A. and Fatemi, M. and Alizad, A. and Viswanathan, V. and Krishnan, P.K. and Naidu, S.",
    journal = "Computers in Biology and Medicine",
    year = "2021",
    volume = "130",
    publisher = "Elsevier Ireland Ltd",
    issn = "0010-4825",
    doi = "10.1016/j.compbiomed.2021.104210",
    keywords = "Behavioral research;  Biological organs;  Computer aided diagnosis;  Computerized tomography;  Damage detection;  Diseases;  Health risks;  Medical imaging;  Respiratory mechanics;  Stability criteria, Acute respiratory distress syndrome;  Clinical validations;  Computer Aided Diagnosis(CAD);  Design considerations;  Multiple imaging modality;  Reliability and stability;  Statistical distribution;  World Health Organization, Artificial intelligence, adult respiratory distress syndrome;  age;  artificial intelligence;  comorbidity;  coronavirus disease 2019;  diagnostic imaging;  feature selection;  human;  intensive care;  interstitial pneumonia;  lung scintiscanning;  nonhuman;  pathophysiology;  pneumonia;  priority journal;  Review;  statistical distribution;  thorax radiography;  tissue characterization;  transfer of learning;  ultrasound;  World Health Organization;  x-ray computed tomography;  diagnostic imaging;  lung;  severity of illness index;  x-ray computed tomography, Artificial Intelligence;  COVID-19;  Humans;  Lung;  SARS-CoV-2;  Severity of Illness Index;  Tomography, X-Ray Computed",
    abstract = "COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings. © 2021 Elsevier Ltd"
}