A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.
ARDS
Artificial intelligence
COVID-19
CT
Comorbidity
Deep learning
Machine learning
Medical imaging
Transfer learning
US
Ultrasound
X-ray
Journal
Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
13
11
2020
revised:
03
01
2021
accepted:
03
01
2021
pubmed:
8
2
2021
medline:
3
3
2021
entrez:
7
2
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33550068
pii: S0010-4825(21)00004-4
doi: 10.1016/j.compbiomed.2021.104210
pmc: PMC7813499
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
104210Subventions
Organisme : NIMH NIH HHS
ID : R44 MH112470
Pays : United States
Organisme : NIMH NIH HHS
ID : R44 MH115523
Pays : United States
Informations de copyright
Copyright © 2021 Elsevier Ltd. All rights reserved.
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