ArticleScientific reports2022
Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- LobePrior segments lung lobes on computed tomography images in the presence of severe abnormalities.Scientific reports · 2026Article
- MSRCTNet: a novel multi-scale capsule triplet network for efficient redundant frame removal in wireless capsule endoscopy videos.Scientific reports · 2026Article
- Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2026Article
- Development and validation of a pneumonia severity prediction model using AI analysis of abdominal and paravertebral intramuscular fat on chest CT in COVID-19 patients.Journal of thoracic disease · 2025Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- COVID-19: An overview on possible transmission ways, sampling matrices and diagnosis.BioImpacts : BI · 2024Review
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans.PloS one · 2023Article
- COVID-WideNet-A capsule network for COVID-19 detection.Applied soft computing · 2022Article
- Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet.Frontiers in microbiology · 2022Article
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Authors and funding
11 authors.
Funding
Abstract
Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.
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