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Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Ultrasound in interstitial lung disease: a bibliometric analysis from 1991 to 2025
Bao-Quan Chen1, Zhengyang Shen2, Hua Liang1
1Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Objectives:
Interstitial lung disease (ILD) comprises a heterogeneous group of diffuse parenchymal lung disorders with a significant clinical burden. Although high-resolution computed tomography (HRCT) remains the imaging gold standard, its limitations have driven interest in ultrasound as a non-invasive, point-of-care alternative for screening, diagnosis, and monitoring.
Methods:
We performed a bibliometric analysis of publications on ultrasound in ILD from 1991 to 2025 to outline the global research landscape, evolving trends, and future directions. Using the Web of Science Core Collection, 608 relevant articles were included and analysed with VOSviewer and CiteSpace.
Results:
The bibliometric analysis revealed sustained growth in research output, with the United States and Italy leading in both publication volume and citation impact. The article entitled "How I do it: Lung ultrasound" has the highest citation count. Keyword cluster analysis identified four major research themes: (1) ultrasound for screening ILD, (2) ultrasound for distinguishing ILD, (3) ultrasound for assessing the severity of ILD, and (4) ultrasound for the management of ILD. Temporal analysis of keyword bursts illustrated a clear evolution from early focus on connective tissue disease-related complications toward ILD-specific sonographic markers, prognostic assessment, and more recently, the integration of artificial intelligence (AI).
Conclusions:
Ultrasound has advanced from an adjunct tool to a key component in ILD assessment, supporting screening, diagnosis, severity stratification, and treatment monitoring. AI integration offers promising pathways toward standardisation and enhanced predictive capability.