Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
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
1 citing paper in PubMed, 5 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
12 authors at 4 institutions in 2 countries.
Dian JiangResearch Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.ORCID 0000-0002-2875-2373
Jianxiang LiaoDepartment of Neurology, Shenzhen Children's Hospital, Shenzhen 518000, China.
Cailei ZhaoDepartment of Radiology, Shenzhen Children's Hospital, Shenzhen 518000, China.
Xia ZhaoDepartment of Neurology, Shenzhen Children's Hospital, Shenzhen 518000, China.
Rongbo LinDepartment of Emergency, Shenzhen Children's Hospital, Shenzhen 518000, China.
Jun YangResearch Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.
Zhi-Cheng LiResearch Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.ORCID 0000-0003-4140-0580
Yihang ZhouResearch Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.
Yanjie ZhuUniversity of Chinese Academy of Sciences, Beijing 100049, China.
Dong LiangResearch Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.
Haifeng WangUniversity of Chinese Academy of Sciences, Beijing 100049, China.ORCID 0000-0003-4229-3668
Shenzhen Children's Hospital · CNChinese Academy of Sciences · CNShenzhen Institutes of Advanced Technology · CNHong Kong Sanatorium and Hospital · HK
Funding
Guangdong High-level Hospital Construction Fund Shenzhen Children's Hospital ynkt2021-zz11Key Field R&D Program of Guangdong Province 2018B030335001Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province 2020B1212060051National Natural Science Foundation of China 6161871373, 62271474, 81729003 and 81901736)Pearl River Talent Recruitment Program of Guangdong Province 2019QN01Y986Sanming Project of Medicine in Shenzhen SZSM201812005Science and Technology Plan Program of Guangzhou 202007030002Shenzhen Science and Technology Program JCYJ20210324115810030Shenzhen Science and Technology Program KQTD20180413181834876Strategic Priority Research Program of Chinese Academy of Sciences XDB25000000 and XDC07040000
6 · The paper itself
Abstract
Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR
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.
Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network. · full record | OpenQuestion