ArticleEuropean radiology2021
Automated quantification of COVID-19 severity and progression using chest CT images.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it, 90 citations in OpenAlex.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Computed tomography-based radiomics improves non-invasive diagnosis of Pneumocystis jirovecii pneumonia in non-HIV patients: a retrospective study.BMC pulmonary medicine · 2024Trial
- HFSUNet: A UNet architecture optimized for precise segmentation of pneumonia lesion boundaries.iScience · 2026Article
- Automated quantitative chest CT for mortality prediction in COVID-19 patients in a resource-limited emergency setting in Gaza: a retrospective study using LungCTAnalyzer.Emergency radiology · 2025Article
- Semi-quantitative software evaluation of COVID-19 CT examinations-correlation with clinical parameters.Journal of thoracic disease · 2025Article
- Generative AI enables medical image segmentation in ultra low-data regimes.Nature communications · 2025Article
- An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation.Scientific reports · 2025Article
- A multi-modal deep learning solution for precise pneumonia diagnosis: the PneumoFusion-Net model.Frontiers in physiology · 2025Article
- CT-Derived Features as Predictors of Clot Burden and Resolution.Bioengineering (Basel, Switzerland) · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Correlation between oxygenation function and laboratory indicators in COVID-19 patients based on non-enhanced chest CT images and construction of an artificial intelligence prediction model.Frontiers in microbiology · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI).European radiology · 2023Article
- Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters.BMC infectious diseases · 2023Article
- The value of lung ultrasound in COVID-19 pneumonia, verified by high resolution computed tomography assessed by artificial intelligence.BMC infectious diseases · 2023Observational
- Prognosticating various acute covid lung disorders from COVID-19 patient using chest CT Images.Engineering applications of artificial intelligence · 2023Article
- Automated semantic lung segmentation in chest CT images using deep neural network.Neural computing & applications · 2023Article
- Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review.Neural computing & applications · 2022Review
- An Entropy-Based Measure of Complexity: An Application in Lung-Damage.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveTo develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans.
methodsOne hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression.
resultsThere was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm
conclusionThe preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. KEY POINTS: • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.
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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.