ArticleInternational journal of environmental research and public health2020
Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 5 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.
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.
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Who cites it
79 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.Medical image analysis · 2021Pooled it
- Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- PARADIM: A Platform to Support Research at the Interface of Data Science and Medical Imaging.Journal of imaging informatics in medicine · 2026Article
- Human-centered evaluation of statistical parametric mapping and explainable machine learning for outlier detection in plantar pressure data.Scientific reports · 2026Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- COVID-19 detection from exhaled breath.Scientific reports · 2024Article
- DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.Sensors (Basel, Switzerland) · 2024Article
- AI pitfalls and what not to do: mitigating bias in AI.The British journal of radiology · 2023Review
- Discovering associations between radiological features and COVID-19 patients' deterioration.Health science reports · 2023Article
- Leveraging human expert image annotations to improve pneumonia differentiation through human knowledge distillation.Scientific reports · 2023Article
- Analysis: Flawed Datasets of Monkeypox Skin Images.Journal of medical systems · 2023Article
- Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability.Scientific reports · 2023Article
- Biases associated with database structure for COVID-19 detection in X-ray images.Scientific reports · 2023Article
- In Search of an Efficient and Reliable Deep Learning Model for Identification of COVID-19 Infection from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19.Evolving systems · 2023Article
- Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.
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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.