SynthesisJournal of infection and public health2022
Medical image processing and COVID-19: A literature review and bibliometric analysis.
Synthesis in Journal of infection and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
- Non-invasive predictive model for incidental gallbladder carcinoma based on multimodal features: Integrating clinical data, MRI radiomics, and deep transfer learning features.Translational oncology · 2026Article
- Global research trends of immunosenescence and immunotherapy: A bibliometric study.Human vaccines & immunotherapeutics · 2025Review
- Comparative analysis of convolutional neural networks and vision transformers in identifying benign and malignant breast lesions.Medicine · 2025Article
- Exploring the changing landscape of medical imaging: insights from highly cited studies before and during the COVID-19 pandemic.European radiology · 2025Article
- Mapping the knowledge landscape of the PET/MR domain: a multidimensional bibliometric analysis.European journal of nuclear medicine and molecular imaging · 2025Review
- Instance-level medical image classification for text-based retrieval in a medical data integration center.Computational and structural biotechnology journal · 2024Article
- Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation.Scientific reports · 2024Article
- Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.BMC medical imaging · 2024Article
- The 100 most-cited and 100 most-mentioned COVID-19-related radiological articles: a comparative bibliometric analysis.European radiology · 2024Article
- Discovering associations between radiological features and COVID-19 patients' deterioration.Health science reports · 2023Article
- Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data.Multimedia tools and applications · 2023Article
- COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context.Telematics and informatics · 2023Article
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
- A Bibliometric Analysis of Personal Protective Equipment and COVID-19 Researches.Frontiers in public health · 2022Review
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
- Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.
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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.