Akademik Veri Yönetim Sistemi
SURGICAL AND RADIOLOGIC ANATOMY, cilt.48, sa.1, 2026 (SCI-Expanded, Scopus)
Purpose This bibliometric review examined trends in the digital analysis of anatomical structures, focusing on volumetric analysis and segmentation studies from 2000 to 2025. The aim was to identify research patterns, collaborations, and emerging themes, and to evaluate the influence of advances in artificial intelligence and medical imaging. Methods Articles indexed in the Web of Science Core Collection were analyzed using VOSviewer and Web of Science Analytics. A total of 3172 publications were assessed for trends in output, citations, authors, institutions, countries, and keyword networks. Results The rise in Artificial Intelligence and deep learning keywords after 2020 coincides with increasing publication output. The most cited authors were Ashburner, J. and Friston, K.J., and the most cited work was "A Survey on Deep Learning in Medical Image Analysis" by Litjens et al. (Med Image Anal 42:60-88, 2017. https://doi.org/10.1016/j.media.2017.07.0 05). Neuroanatomical research dominated, with the hippocampus and the brain as the most-studied structures. MRI, deep learning, and segmentation were leading keywords. Harvard University and the University of Johns Hopkins University ranked highest in productivity, while Neuroimage and IEEE Transactions on Medical Imaging ranked highest in influence. The USA led in both publication output and citations. Conclusion Artificial Intelligence-driven digital technologies are increasingly shaping anatomical research. Despite a strong focus on neuroscience, peripheral organs and non-neurological applications remain underexplored, offering opportunities for future work in fields such as anatomy, orthopedics, and dentistry.