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Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study
The number of scholarly articles continues to rise. The continuous increase in scientific output poses a challenge for researchers, who must devote considerable time to collecting and analyzing these results. The topic modeling approach emerges as a novel response to this need. Considering the swift...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661298/ https://www.ncbi.nlm.nih.gov/pubmed/37987344 http://dx.doi.org/10.3390/tomography9060158 |
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author | Ozkara, Burak B. Karabacak, Mert Margetis, Konstantinos Yedavalli, Vivek S. Wintermark, Max Bisdas, Sotirios |
author_facet | Ozkara, Burak B. Karabacak, Mert Margetis, Konstantinos Yedavalli, Vivek S. Wintermark, Max Bisdas, Sotirios |
author_sort | Ozkara, Burak B. |
collection | PubMed |
description | The number of scholarly articles continues to rise. The continuous increase in scientific output poses a challenge for researchers, who must devote considerable time to collecting and analyzing these results. The topic modeling approach emerges as a novel response to this need. Considering the swift advancements in computed tomography perfusion (CTP), we deem it essential to launch an initiative focused on topic modeling. We conducted a comprehensive search of the Scopus database from 1 January 2000 to 16 August 2023, to identify relevant articles about CTP. Using the BERTopic model, we derived a group of topics along with their respective representative articles. For the 2020s, linear regression models were used to identify and interpret trending topics. From the most to the least prevalent, the topics that were identified include “Tumor Vascularity”, “Stroke Assessment”, “Myocardial Perfusion”, “Intracerebral Hemorrhage”, “Imaging Optimization”, “Reperfusion Therapy”, “Postprocessing”, “Carotid Artery Disease”, “Seizures”, “Hemorrhagic Transformation”, “Artificial Intelligence”, and “Moyamoya Disease”. The model provided insights into the trends of the current decade, highlighting “Postprocessing” and “Artificial Intelligence” as the most trending topics. |
format | Online Article Text |
id | pubmed-10661298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106612982023-11-01 Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study Ozkara, Burak B. Karabacak, Mert Margetis, Konstantinos Yedavalli, Vivek S. Wintermark, Max Bisdas, Sotirios Tomography Review The number of scholarly articles continues to rise. The continuous increase in scientific output poses a challenge for researchers, who must devote considerable time to collecting and analyzing these results. The topic modeling approach emerges as a novel response to this need. Considering the swift advancements in computed tomography perfusion (CTP), we deem it essential to launch an initiative focused on topic modeling. We conducted a comprehensive search of the Scopus database from 1 January 2000 to 16 August 2023, to identify relevant articles about CTP. Using the BERTopic model, we derived a group of topics along with their respective representative articles. For the 2020s, linear regression models were used to identify and interpret trending topics. From the most to the least prevalent, the topics that were identified include “Tumor Vascularity”, “Stroke Assessment”, “Myocardial Perfusion”, “Intracerebral Hemorrhage”, “Imaging Optimization”, “Reperfusion Therapy”, “Postprocessing”, “Carotid Artery Disease”, “Seizures”, “Hemorrhagic Transformation”, “Artificial Intelligence”, and “Moyamoya Disease”. The model provided insights into the trends of the current decade, highlighting “Postprocessing” and “Artificial Intelligence” as the most trending topics. MDPI 2023-11-01 /pmc/articles/PMC10661298/ /pubmed/37987344 http://dx.doi.org/10.3390/tomography9060158 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Ozkara, Burak B. Karabacak, Mert Margetis, Konstantinos Yedavalli, Vivek S. Wintermark, Max Bisdas, Sotirios Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study |
title | Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study |
title_full | Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study |
title_fullStr | Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study |
title_full_unstemmed | Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study |
title_short | Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study |
title_sort | assessment of computed tomography perfusion research landscape: a topic modeling study |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661298/ https://www.ncbi.nlm.nih.gov/pubmed/37987344 http://dx.doi.org/10.3390/tomography9060158 |
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