ArticleAnnals of translational medicine2021
Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers.European journal of radiology · 2021Pooled it
- Early Immunological Biomarkers for Personalized Treatment Selection in Severe COVID-19: Post Hoc Machine Learning Analysis of a Randomized Clinical Trial.JMIR medical informatics · 2026Article
- Optimising strategies for artificial intelligence-assisted classification of viral pneumonias on CT imaging: a comparative study of selective and default approaches.Polish journal of radiology · 2025Article
- Improve the performance of CT-based pneumonia classification via source data reweighting.Scientific reports · 2023Article
- Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches.European radiology experimental · 2023Article
- Nurse, Give Me the News! Understanding Support for and Opposition to a COVID-19 Health Screening System.International journal of environmental research and public health · 2023Article
- Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.Tomography (Ann Arbor, Mich.) · 2022Article
- SARS-CoV-2: Has artificial intelligence stood the test of time.Chinese medical journal · 2022Article
- External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.Radiology. Artificial intelligence · 2022Article
- Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study.European journal of radiology open · 2022Article
- ADA-COVID: Adversarial Deep Domain Adaptation-Based Diagnosis of COVID-19 from Lung CT Scans Using Triplet Embeddings.Computational intelligence and neuroscience · 2022Article
- Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- Clinical characteristics of patients with confirmed and suspected COVID-19.American journal of translational research · 2021Article
- Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.Computational and structural biotechnology journal · 2021Review
- COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios.Computer methods and programs in biomedicine · 2020Article
- Prediction of COVID-19 Using Genetic Deep Learning Convolutional Neural Network (GDCNN).IEEE access : practical innovations, open solutions · 2020Article
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Authors and funding
20 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP).
methodsA total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves.
resultsOf the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87
conclusionsOur study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.
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