ArticleNPJ digital medicine2021
CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- 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
- Artificial intelligence model outperformed experienced clinicians in differentiating the aetiology of pneumonia on chest computed tomography: a retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- Utility of the virtual imaging trials methodology for objective characterization of AI systems and training data.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Evaluation of Chest CT Scan Findings in Pediatric Patients With COVID-19: A Retrospective Descriptive Study.Health science reports · 2025Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition.Interdisciplinary sciences, computational life sciences · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections.Innovation (Cambridge (Mass.)) · 2024Article
- DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.Sensors (Basel, Switzerland) · 2024Article
- A lightweight neural network for lung nodule detection based on improved ghost module.Quantitative imaging in medicine and surgery · 2023Article
- COVID-19 diagnosis utilizing wavelet-based contrastive learning with chest CT images.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- Towards precision medicine: Omics approach for COVID-19.Biosafety and health · 2023Review
- Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.Computers in biology and medicine · 2023Article
- Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.Biomedical signal processing and control · 2023Article
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images.Computational intelligence and neuroscience · 2023Article
- COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans.Cognitive computation · 2022Article
Corrections and comments
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Authors and funding
22 authors.
Funding
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Abstract
Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.
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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.