Evidence map›Paper›PMID 34749621›Full record

ReviewCurrent neuropharmacology2022

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges.

Xi Chen, Yu Lei, Jiabin Su, Heng Yang, Wei Ni, Jinhua Yu, Yuxiang Gu, Ying Mao

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current neuropharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. [Automatic detection method of intracranial aneurysms on maximum intensity projection images based on SE-CaraNet].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024
    Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Review
4 · The record

Corrections and comments

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

8 authors at 2 institutions in 1 country.

Xi ChenSchool of Information Science and Technology, Fudan University, Shanghai, China.
Yu LeiDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Jiabin SuDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Heng YangDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Wei NiDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Jinhua YuSchool of Information Science and Technology, Fudan University, Shanghai, China.
Yuxiang GuDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Ying MaoDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Fudan University · CNHuashan Hospital · CN

Funding

National Natural Science Foundation of China (NSFC) 81801155, 81771237New Technology Projects of Shanghai Science and Technology Innovation Action Plan 18511102800Shanghai Municipal Science and Technology Major Project and ZJLab 2018SHZDZX01
6 · The paper itself

Abstract

backgroundA variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences.

objectiveThis work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases.

methodsBased on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases.

resultsFor the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion.

conclusionAlgorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

Indexed as

Arteriovenous MalformationsMoyamoya DiseaseArtificial IntelligenceHumansMachine Learningartificial intelligenceCerebrovascular diseasescomputer-aided detectioncomputer-aided predictioncomputer-aided treatment decisionmachine learning

Identifiers

PMID34749621
PMCPMC9881077
OpenAlexW3212022870

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.