ArticleScientific reports2020
Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 246 papers, 1 of them a synthesis that pooled it.
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246 citing papers in PubMed, 1 synthesis or guideline pooled it, 446 citations in OpenAlex.
- Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis.Academic radiology · 2021Pooled it
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- Integrated Deep Learning Surveillance of Unknown Pathogens with Pandemic Potential Using Pneumonia of Unknown Etiology.Pathogens (Basel, Switzerland) · 2026Article
- The Impact of CT Reconstruction Parameters on Emphysema Index Quantification, HU-Based Measurements, and Goddard Score in COPD Assessment: A Prospective Study.International journal of biomedical imaging · 2026Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.Scientific reports · 2025Article
- Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation.NPJ digital medicine · 2024Article
- Enhanced COVID-19 Detection from X-ray Images with Convolutional Neural Network and Transfer Learning.Journal of imaging · 2024Article
- A predictive model to explore risk factors for severe COVID-19.Scientific reports · 2024Article
- Deep-learning map segmentation for protein X-ray crystallographic structure determination.Acta crystallographica. Section D, Structural biology · 2024Article
- Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network.Diagnostics (Basel, Switzerland) · 2024Article
- Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs).Scientific reports · 2024Article
- Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images.Journal of imaging · 2024Article
- COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning.PloS one · 2024Article
- Contrastive learning with token projection for Omicron pneumonia identification from few-shot chest CT images.Frontiers in medicine · 2024Article
- The impact of geo-political socio-economic factors on vaccine dissemination trends: a case-study on COVID-19 vaccination strategies.BMC public health · 2023Article
- Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.Health care management science · 2023Article
186 more citing papers are in PubMed but not listed here.
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Authors and funding
24 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
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