ReviewComputers in biology and medicine2021
Artificial intelligence-driven assessment of radiological images for COVID-19.
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 27 papers.
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Who cites it
27 citing papers in PubMed.
- Unlocking the potential: multimodal AI in biotechnology and digital medicine-economic impact and ethical challenges.NPJ digital medicine · 2025Review
- Artificial Intelligence in Infectious Disease Diagnostic Technologies.Diagnostics (Basel, Switzerland) · 2025Review
- New scoring system combining WHO ordinal scale and MASCC score in predicting mortality of COVID-19 patients with malignancy and neutropenia: a retrospective cohort study.BMC infectious diseases · 2025Article
- Chest computed tomography for patients with sepsis in the emergency intensive care unit.Scientific data · 2024Article
- Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.Radiology. Artificial intelligence · 2024Article
- Advancing acute respiratory failure management through artificial intelligence: a call for thematic collection contributions.Intensive care medicine experimental · 2024Article
- Generalizable disease detection using model ensemble on chest X-ray images.Scientific reports · 2024Article
- A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study.Frontiers in cellular and infection microbiology · 2024Article
- Development of a novel machine learning model based on laboratory and imaging indices to predict acute cardiac injury in cancer patients with COVID-19 infection: a retrospective observational study.Journal of cancer research and clinical oncology · 2023Observational
- Artificial Intelligence Applied to Chest X-ray: A Reliable Tool to Assess the Differential Diagnosis of Lung Pneumonia in the Emergency Department.Diseases (Basel, Switzerland) · 2023Article
- Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19.Scientific reports · 2023Article
- MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images.Expert systems with applications · 2023Article
- Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.Computers in biology and medicine · 2023Article
- Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation.Biomedical signal processing and control · 2023Article
- Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.Quantitative imaging in medicine and surgery · 2023Article
- A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images.Biomedical signal processing and control · 2023Article
- Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision.Expert systems with applications · 2022Article
- Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.Quantitative imaging in medicine and surgery · 2022Article
- High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms.Scientific reports · 2022Article
- An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.Academic radiology · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.
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