Malawi medical journal : the journal of Medical Association of Malawi · 2026
Bibliometric Analysis of Pulmonary Fibrosis Imaging Research: Knowledge Graph Construction Based on the Web of Science Core Database.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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Ошибки и ограничения
История · было → стало
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Yu Shuo, Li Zhiyue, Zhang Yuting, Yan Yaqi, Tian Min, Bian Yuhan, Bahadori Pardis, Jin Chenwang
Аннотация
AIM: To quantify publication trends, map international collaboration networks, and identify dominant and emerging research themes in Pulmonary fibrosis(PF) imaging (2015-2024).
METHODS: A structured Web of Science search combining PF- and imaging-related terms yielded 1,159 English-language original research articles. Analyses employed CiteSpace, VOSviewer, and Scimago Graphica for trend assessment, keyword co-occurrence, citation burst detection, and collaboration network visualization.
RESULTS: Annual publication volume showed sustained linear growth (R2 = 0.867). Researchers from 64 countries contributed; the United States led in output (332 publications) and citation impact (9,821 citations), while China ranked second in volume (224 publications) with lower proportional citation impact. The Western Europe-North America axis showed the densest collaborative ties. Keyword co-occurrence revealed close thematic links among idiopathic pulmonary fibrosis, high-resolution computed tomography (HRCT), usual interstitial pneumonia (UIP), survival, and mortality. Citation burst analysis identified "deep learning" as the strongest and most sustained burst (2021-2024), by which point it had shifted from exploratory method to established domain. Three overlapping research phases emerged: diagnostic framework consolidation (2015-2018), computational computed tomography (CT)-based phenotyping (2016-2022), and therapeutic expansion toward antifibrotics and progressive fibrosing interstitial lung disease (2019-2024).
CONCLUSION: PF imaging research has shifted from diagnostic consensus toward quantitative CT biomarkers and artificial intelligence(AI)-driven phenotyping, driven by the need to reduce interobserver variability and enable individualized risk stratification. Geographic fragmentation and limited multicenter validation remain key barriers to AI generalizability. Future priorities include standardized imaging protocols, prospective multicenter validation cohorts, and integration of AI-driven CT phenotyping with multi-omics and circulating biomarkers for prognostic precision.
METHODS: A structured Web of Science search combining PF- and imaging-related terms yielded 1,159 English-language original research articles. Analyses employed CiteSpace, VOSviewer, and Scimago Graphica for trend assessment, keyword co-occurrence, citation burst detection, and collaboration network visualization.
RESULTS: Annual publication volume showed sustained linear growth (R2 = 0.867). Researchers from 64 countries contributed; the United States led in output (332 publications) and citation impact (9,821 citations), while China ranked second in volume (224 publications) with lower proportional citation impact. The Western Europe-North America axis showed the densest collaborative ties. Keyword co-occurrence revealed close thematic links among idiopathic pulmonary fibrosis, high-resolution computed tomography (HRCT), usual interstitial pneumonia (UIP), survival, and mortality. Citation burst analysis identified "deep learning" as the strongest and most sustained burst (2021-2024), by which point it had shifted from exploratory method to established domain. Three overlapping research phases emerged: diagnostic framework consolidation (2015-2018), computational computed tomography (CT)-based phenotyping (2016-2022), and therapeutic expansion toward antifibrotics and progressive fibrosing interstitial lung disease (2019-2024).
CONCLUSION: PF imaging research has shifted from diagnostic consensus toward quantitative CT biomarkers and artificial intelligence(AI)-driven phenotyping, driven by the need to reduce interobserver variability and enable individualized risk stratification. Geographic fragmentation and limited multicenter validation remain key barriers to AI generalizability. Future priorities include standardized imaging protocols, prospective multicenter validation cohorts, and integration of AI-driven CT phenotyping with multi-omics and circulating biomarkers for prognostic precision.