دورية أكاديمية
أعرض في EDS

Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review.

التفاصيل البيبلوغرافية
العنوان: Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review.
المؤلفون: Uparela-Reyes MJ; Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia. Electronic address: maria.uparela@correounivalle.edu.co., Villegas-Trujillo LM; Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; School of Biomedical Sciences, Universidad del Valle, Cali, Colombia., Cespedes J; Comprehensive Epilepsy Center, Yale University, New Haven, Connecticut, USA., Velásquez-Vera M; Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia., Rubiano AM; Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia; INUB-Meditech Research Group, Neurosciences Institute, Universidad El Bosque, Bogotá, Colombia.
المصدر: World neurosurgery [World Neurosurg] 2024 Aug; Vol. 188, pp. 83-92. Date of Electronic Publication: 2024 May 15.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: Elsevier Country of Publication: United States NLM ID: 101528275 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-8769 (Electronic) Linking ISSN: 18788750 NLM ISO Abbreviation: World Neurosurg Subsets: MEDLINE
أسماء مطبوعة: Original Publication: New York : Elsevier
مواضيع طبية MeSH: Brain Injuries, Traumatic*/diagnostic imaging , Bibliometrics* , Artificial Intelligence*, Humans ; Machine Learning
مستخلص: 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: 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 key words, countries co-authorship, and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies.
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 were those with the greatest number of publications. The most frequently co-occurring key words 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.
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.
(Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
فهرسة مساهمة: Keywords: Artificial intelligence; Brain trauma; Deep learning; Head trauma; Machine learning; Traumatic brain injury
تواريخ الأحداث: Date Created: 20240517 Date Completed: 20240716 Latest Revision: 20240716
رمز التحديث: 20240716
DOI: 10.1016/j.wneu.2024.05.065
PMID: 38759786
قاعدة البيانات: MEDLINE
الوصف
تدمد:1878-8769
DOI:10.1016/j.wneu.2024.05.065