Abstract

Introduction: Deep learning has emerged as a promising approach for automated bone age assessment; however, the scientific development of this field has not been comprehensively characterized. Materials and Methods: A bibliometric analysis of studies published between 2015 and 2025 was conducted using data from Web of Science, Scopus, MEDLINE (PubMed), IEEE Xplore, and Google Scholar. A total of 67 studies were included. Bibliometric mapping, network visualization, and knowledge structure analyses were performed using VOSviewer® (v. 1.6.20) and CiteSpace® (v. 6.3.R1). Results: Scientific output increased markedly, from 1 publication in 2015–2016 to 25 in 2025. The period 2019–2020 achieved the highest citation impact (1191 citations across 18 studies). China was the leading contributor, followed by the United States, South Korea, and Turkey. Keyword analysis identified convolutional neural networks, the RSNA dataset, and the Greulich–Pyle method as central research themes. Recent studies have increasingly focused on external validation, multicenter datasets, and transformer-based architectures. Conclusions: Research on deep learning for bone age assessment has grown substantially over the past decade. This bibliometric analysis highlights the field’s major contributors, influential publications, and emerging trends, while emphasizing the need for greater validation, dataset diversity, and standardized reporting.

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