USEFULNESS OF ARTIFICIAL INTELLIGENCE IN TRAUMATIC BRAIN INJURY: A BIBLIOMETRIC ANALYSIS AND MINIREVIEW.
"artificial intelligence"
"brain trauma"
"deep learning"
"traumatic brain injury"
“Machine learning"
“head trauma"
Journal
World neurosurgery
ISSN: 1878-8769
Titre abrégé: World Neurosurg
Pays: United States
ID NLM: 101528275
Informations de publication
Date de publication:
15 May 2024
15 May 2024
Historique:
received:
08
03
2024
accepted:
10
05
2024
medline:
18
5
2024
pubmed:
18
5
2024
entrez:
17
5
2024
Statut:
aheadofprint
Résumé
Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use Artificial Intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and Mini Review on the main uses that have been developed for TBI in AI. The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of keywords, countries co-authorship and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies. A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and Plos One where those with the greatest number of publications. The most frequently co-occurring keywords were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles. AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.
Sections du résumé
BACKGROUND
BACKGROUND
Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use Artificial Intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and Mini Review on the main uses that have been developed for TBI in AI.
METHODS
METHODS
The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of keywords, countries co-authorship and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies.
RESULTS
RESULTS
A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and Plos One where those with the greatest number of publications. The most frequently co-occurring keywords were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles.
CONCLUSION
CONCLUSIONS
AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.
Identifiants
pubmed: 38759786
pii: S1878-8750(24)00828-3
doi: 10.1016/j.wneu.2024.05.065
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024. Published by Elsevier Inc.