ReviewMedicine2021
Application of machine learning in CT images and X-rays of COVID-19 pneumonia.
Review in Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed, 53 citations in OpenAlex.
- Personalized decision-making through AI solutions in pediatric emergency medicine: Focusing on febrile children.PLOS digital health · 2025Review
- Healthcare-Associated Infections: The Role of Microbial and Environmental Factors in Infection Control-A Narrative Review.Infectious diseases and therapy · 2025Review
- Optimization of Soft X-Ray Fresnel Zone Plate Fabrication Through Joint Electron Beam Lithography and Cryo-Etching Techniques.Nanomaterials (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives.Biomedicines · 2024Review
- Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley.Plant methods · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- A multi-output network with U-net enhanced class activation map and robust classification performance for medical imaging analysis.Discover artificial intelligence · 2023Article
- Article
- Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?Journal of clinical medicine · 2022Article
- Asthma Hospital Admission and Readmission Spikes, Advancing Accurate Classification to Advance Understanding of Causes.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.Journal of clinical medicine · 2022Review
- Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population.Frontiers in medicine · 2022Article
- Detection of Pneumonia Infection by Using Deep Learning on a Mobile Platform.Computational intelligence and neuroscience · 2022Article
- Image Recognition of Pediatric Pneumonia Based on Fusion of Texture Features and Depth Features.Computational and mathematical methods in medicine · 2022Article
- The Two Weapons against Bacterial Biofilms: Detection and Treatment.Antibiotics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
abstractCoronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-ray and CT images, assist doctors in improving diagnosis efficiency, and facilitate the subsequent assessment of the severity of the patient infection. The medical assistant platform based on machine learning can help radiologists make clinical decisions and helper in screening, diagnosis, and treatment. By providing scientific methods for image recognition, segmentation, and evaluation, we summarized the latest developments in the application of artificial intelligence in COVID-19 lung imaging, and provided guidance and inspiration to researchers and doctors who are fighting the COVID-19 virus.
Indexed as
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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.