ReviewComputers in biology and medicine2021
A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.
Review in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.IEEE journal of biomedical and health informatics · 2021Pooled it
- Immunological Mechanisms and Machine Learning Applications in Post-COVID-19 Syndrome: A Narrative Review.Microorganisms · 2026Article
- Artificial Intelligence in Infectious Disease Diagnostic Technologies.Diagnostics (Basel, Switzerland) · 2025Review
- Chest computed tomography for patients with sepsis in the emergency intensive care unit.Scientific data · 2024Article
- Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.Diagnostics (Basel, Switzerland) · 2024Article
- Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.Healthcare (Basel, Switzerland) · 2024Article
- Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.The international journal of cardiovascular imaging · 2024Article
- Advancing acute respiratory failure management through artificial intelligence: a call for thematic collection contributions.Intensive care medicine experimental · 2024Article
- Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool.Cureus · 2024Review
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Lung Imaging and Artificial Intelligence in ARDS.Journal of clinical medicine · 2024Review
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19 and protection of vaccination in patients with systemic sclerosis-associated interstitial lung disease.Journal of scleroderma and related disorders · 2023Article
- Profiling Covid-19 patients with respect to level of severity: an integrated statistical approach.Scientific reports · 2023Article
- A comprehensive review of SARS-CoV-2 vaccines: Pfizer, Moderna & Johnson & Johnson.Human vaccines & immunotherapeutics · 2022Review
- Article
- Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity.Diagnostics (Basel, Switzerland) · 2022Article
- COVID-19: impact on Public Health and hypothesis-driven investigations on genetic susceptibility and severity.Immunogenetics · 2022Review
- Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.Computers in biology and medicine · 2022Article
Corrections and comments
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Authors and funding
46 authors at 20 institutions in 10 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.