ArticleMedical image analysis2022
Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Pooled it
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition.Nature communications · 2025Article
- Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators.Scientific data · 2025Article
- Real-world federated learning in radiology: hurdles to overcome and benefits to gain.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- CT-based Assessment at 6-Month Follow-up of COVID-19 Pneumonia patients in China.Scientific reports · 2024Article
- COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.Sensors (Basel, Switzerland) · 2024Review
- Lesion region inpainting: an approach for pseudo-healthy image synthesis in intracranial infection imaging.Frontiers in microbiology · 2024Article
- Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.Journal of imaging · 2023Article
- Multimodal graph attention network for COVID-19 outcome prediction.Scientific reports · 2023Article
- Pulmonary contusion: automated deep learning-based quantitative visualization.Emergency radiology · 2023Article
- CT findings in "Post-Covid": residua from acute pneumonia or "Post-Covid-ILD"?Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2023Article
- CaraNet: context axial reverse attention network for segmentation of small medical objects.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.International journal of imaging systems and technology · 2023Article
- Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.Frontiers in cellular and infection microbiology · 2023Article
- Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation.Frontiers in radiology · 2023Article
- Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation.Medical image analysis · 2022Article
- An ML prediction model based on clinical parameters and automated CT scan features for COVID-19 patients.Scientific reports · 2022Article
- HFCF-Net: A hybrid-feature cross fusion network for COVID-19 lesion segmentation from CT volumetric images.Medical physics · 2022Article
- SR-CycleGAN: super-resolution of clinical CT to micro-CT level with multi-modality super-resolution loss.Journal of medical imaging (Bellingham, Wash.) · 2022Article
Corrections and comments
- Update of
Authors and funding
36 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.
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.