SynthesisJournal of healthcare engineering2021
Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.
Synthesis in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 46 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
46 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review.JMIR mHealth and uHealth · 2024Pooled it
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Automatic Childhood Pneumonia Diagnosis Based on Multi-Model Feature Fusion Using Chi-Square Feature Selection.Journal of imaging · 2026Article
- Hybrid feature selection with novel deep learning model for COVID-19 risk prediction.Scientific reports · 2026Article
- Light-induced field effect transistor-based biosensor consisted of aptamer and ReSMaterials today. Bio · 2025Article
- An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics.Archives of public health = Archives belges de sante publique · 2025Article
- Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education.Sensors (Basel, Switzerland) · 2024Article
- An architecture for COVID-19 analysis and detection using big data, AI, and data architectures.PloS one · 2024Article
- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI).Journal of clinical medicine · 2023Article
- Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.Sensors (Basel, Switzerland) · 2023Article
- COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images.BMC medical imaging · 2023Article
- Article
- Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.Sensors (Basel, Switzerland) · 2023Article
- Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images.Intelligent systems with applications · 2023Article
- COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm.Chaos, solitons, and fractals · 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
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Result: This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. Conclusion: The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.
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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.