ReviewComputers in biology and medicine2020
COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.
Review in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19.Sensors (Basel, Switzerland) · 2022Pooled it
- 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
- GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.Scientific reports · 2024Article
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- 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
- Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data.Diagnostics (Basel, Switzerland) · 2023Article
- Common Biomarkers Associated with Delirium in Hospitalized Patients with COVID-19 at the Epicentre of Turkish Coronavirus Outbreak: A Case-Control Study.Noro psikiyatri arsivi · 2023Article
- Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.Healthcare (Basel, Switzerland) · 2022Article
- Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients.Medical & biological engineering & computing · 2022Article
- Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.Journal of clinical medicine · 2022Review
- Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity.Diagnostics (Basel, Switzerland) · 2022Article
- Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm.Journal of cardiovascular development and disease · 2022Article
- Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans.Diagnostics (Basel, Switzerland) · 2022Article
- Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.Cancers · 2022Review
- Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation.Journal of medical systems · 2022Article
- Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.Journal of cardiovascular development and disease · 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
- Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
43 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.
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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.