ReviewDiagnostic and interventional radiology (Ankara, Turkey)2020
A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.
Review in Diagnostic and interventional radiology (Ankara, Turkey), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed, 58 citations in OpenAlex.
- Leveraging Artificial Intelligence and Gene Expression Analysis to Identify Some Potential Bovine Coronavirus (BCoV) Receptors and Host Cell Enzymes Potentially Involved in the Viral Replication and Tissue Tropism.International journal of molecular sciences · 2025Article
- Comparison of image-based modified Ferriman-Gallway score evaluation with in-person evaluation: an alternative method for hirsutism diagnosis.Archives of dermatological research · 2023Article
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images.Pathogens (Basel, Switzerland) · 2022Article
- Identifying urethral strictures using machine learning: a proof-of-concept evaluation of convolutional neural network model.World journal of urology · 2022Article
- COVID-19 pneumonia: lessons learned, challenges, and preparing for the future.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Article
- Artificial Intelligence for COVID-19 Detection in Medical Imaging-Diagnostic Measures and Wasting-A Systematic Umbrella Review.Journal of clinical medicine · 2022Review
- A framework for information technology-based management against COVID-19 in Iran.BMC public health · 2022Article
- Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies.Briefings in bioinformatics · 2022Review
- A comprehensive review on efficient approaches for combating coronaviruses.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021Review
- Can Deep Learning-Based Volumetric Analysis Predict Oxygen Demand Increase in Patients with COVID-19 Pneumonia?Medicina (Kaunas, Lithuania) · 2021Article
- Detection and characterization of COVID-19 findings in chest CT: Feasibility and applicability of an AI-based software tool.Medicine · 2021Observational
- Using Artificial Intelligence in Fungal Lung Disease: CPA CT Imaging as an Example.Mycopathologia · 2021Article
- How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.Reviews in medical virology · 2021Review
- Imaging of COVID-19: An update of current evidences.Diagnostic and interventional imaging · 2021Review
- Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia.BMJ open respiratory research · 2021Article
- Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images.Scientific reports · 2021Article
- The Role of Medical Imaging in COVID-19.Advances in experimental medicine and biology · 2021Article
- Role of Chest CT in COVID-19.Journal of clinical imaging science · 2021Review
- Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively.
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What OpenQuestion holds
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.