ArticleJournal of medical imaging (Bellingham, Wash.)2021
Deep CNN models for predicting COVID-19 in CT and x-ray images.
Article in Journal of medical imaging (Bellingham, Wash.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed, 48 citations in OpenAlex.
- Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.New microbes and new infections · 2026Article
- Improving CNN predictive accuracy in COVID-19 health analytics.Scientific reports · 2025Article
- Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.Narra J · 2025Review
- Homogenization of multi-institutional chest x-ray images in various data transformation schemes.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Deep Survival Analysis With Clinical Variables for COVID-19.IEEE journal of translational engineering in health and medicine · 2023Article
- DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images.Wireless personal communications · 2023Article
- A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.Advances in engineering software (Barking, London, England : 1992) · 2023Article
- Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.Sensors (Basel, Switzerland) · 2022Review
- Augmentation of literature review of COVID-19 radiology.World journal of radiology · 2022Article
- Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images.Neurocomputing · 2022Article
- COVLIAS 1.0Diagnostics (Basel, Switzerland) · 2022Article
- Comparative imaging findings among different primary betacoronaviruses.Polish journal of radiology · 2022Review
- Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs.Biomedical signal processing and control · 2022Article
- COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts.Diagnostics (Basel, Switzerland) · 2021Article
- Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial intelligence-driven assessment of radiological images for COVID-19.Computers in biology and medicine · 2021Review
- COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
What OpenQuestion holds
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.