ReviewDigital health
A systematic review of deep learning-based segmentation techniques for brain tumor detection (2013-2023).
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To investigate the publication trends and research landscape of deep learning-based image segmentation techniques for brain tumor detection, focusing on the period from 2013 to 2023, and to identify key stakeholders, influential research, and prevalent research themes. Methods: This systematic review utilized the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and bibliometric analysis methods. Databases including Scopus, PubMed, and Web of Science were searched for publications from 2013 to 2023 using a defined search query. Analysis included publication trends, stakeholder identification (authors, institutions, funding bodies, countries), top-cited literature, co-authorship, keyword co-occurrence, and citation analysis using VOSviewer software and the results of the PubMed were analyzed by R Studio. Results: A total of 931 documents were analyzed after PRISMA filtering. The number of publications increased substantially from 1 to 310 during the review period. Journal articles were the predominant document type. Tongxue Zhou was the most prolific researcher, and the Ministry of Education of the People's Republic of China, Imperial College of London, and Harvard Medical School were the most active affiliations. The National Natural Science Foundation of China was the leading funding organization. Keyword co-occurrence analysis highlighted the prevalence of "deep learning brain tumor image segmentation." Co-authorship and citation analyses revealed key collaborations and influential publications. Conclusion: This study provides valuable insights into the research landscape of deep learning for brain tumor image segmentation. The identified trends, stakeholders, and research themes can inform future research directions and policy decisions in this rapidly evolving field. The findings highlight the growth and multidisciplinary nature of this research area, while also suggesting potential opportunities and challenges for future development in brain tumor image segmentation techniques.
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.