Evidence map›Paper›PMID 34100010›Full record

ArticleResearch square2021

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Holger R Roth, Ziyue Xu, Carlos Tor Diez, Ramon Sanchez Jacob, Jonathan Zember, Jose Molto, Wenqi Li, Sheng Xu, Baris Turkbey, Evrim Turkbey and 26 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

36 authors.

Holger R RothNVIDIA, Bethesda, MD, USA.ORCID 0000-0002-3662-8743
Ziyue XuNVIDIA, Bethesda, MD, USA.
Carlos Tor DiezSheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA.
Ramon Sanchez JacobDivision of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA.
Jonathan ZemberDivision of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA.
Jose MoltoDivision of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA.
Wenqi LiNVIDIA, Bethesda, MD, USA.
Sheng XuRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Baris TurkbeyRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.ORCID 0000-0003-0853-6494
Evrim TurkbeyRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Dong YangNVIDIA, Bethesda, MD, USA.ORCID 0000-0002-5031-4337
Ahmed HarouniNVIDIA, Bethesda, MD, USA.
Nicola RiekeNVIDIA, Bethesda, MD, USA.
Shishuai HuSchool of Computer Science and Engineering, Northwestern Polytechnical University, China.
Fabian IsenseeHIP Applied Computer Vision Lab, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Claire TangLynbrook High School, San Jose, CA, USA.
Qinji YuShanghai Jiao Tong University, China.
Jan SölterLuxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg.
Tong ZhengSchool of Informatics, Nagoya University, Japan.
Vitali LiauchukBiomedical Image Analysis Department, United Institute of Informatics Problems, Belarus.
Ziqi ZhouGuangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, China.
Jan Hendrik MoltzFraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
Bruno OliveiraLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
Yong XiaSchool of Computer Science and Engineering, Northwestern Polytechnical University, China.ORCID 0000-0001-9273-2847
Klaus H Maier-HeinDivision of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Qikai LiShanghai Jiao Tong University, China.
Andreas HuschFraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany.ORCID 0000-0001-9404-5127
Luyang ZhangSchool of Informatics, Nagoya University, Japan.
Vassili KovalevBiomedical Image Analysis Department, United Institute of Informatics Problems, Belarus.
Li KangGuangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, China.
Alessa HeringFraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany.
João L Vilaça2Ai - Polytechnic Institute of Cávado and Ave, Barcelos, Portugal.
Mona FloresNVIDIA, Bethesda, MD, USA.ORCID 0000-0002-7362-3044
Daguang XuNVIDIA, Bethesda, MD, USA.
Bradford WoodRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.ORCID 0000-0002-4297-0051
Marius George LinguraruSheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA.

Funding

Development of COVID-19 Imaging Tools with Artificial IntelligenceZIABC011936 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI WOOD, BRADFORD J · 2020 to 2025
$1.2M
Interventional OncologyZIACL040015 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2009 to 2025
$0k
Artificial Intelligence with Chest Imaging in COVID-19 and Isolation and Ventilator Devices for COVID-19ZIACL090072 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2020 to 2025
$0k
6 · The paper itself

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.

Identifiers

PMID34100010
PMCPMC8183044

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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.