Global Trends in Ultrasonography Research in Veterinary Anatomy
Abstract views: 39
/ 
PDF downloads: 32
Keywords:
Veterinary Anatomy, Ultrasonography, USGAbstract
This study presents a bibliometric analysis of global trends in ultrasonography (USG) studies
in veterinary anatomy. The aim of this study is to investigate publications related to USG studies in
veterinary anatomy worldwide and to analyse global trends and groups in this field. This bibliometric
study investigates ultrasound studies in veterinary anatomy conducted worldwide between 1983 and
2025. To this end, 715 studies were examined as a result of searches conducted in the Web of Science
(WOS) database using the keywords ‘veterinary anatomy, ultrasonography, USG’. After excluding
ineligible studies and non-article studies, 693 articles were analysed. Information such as title, author
names, publication year, journal name, and number of citations was used for data collection. All text data
was analysed using VOSviewer software to ensure accuracy and reliability. In this study, analyses using
text mining and data visualisation methods (e.g., bubble maps) helped to make the results more
understandable. A total of 13,452 citations were found for the 693 articles examined in the WOS
database. The average number of citations per article is 19, and the H-index is 53. Since 1993, there has
been a significant increase in both the number of articles and citations. The majority of the articles (87%)
were published in the fields of veterinary sciences, radiology and nuclear medicine, agriculture and
zoology, indicating the increasing prevalence of USG studies in these areas. The countries with the
highest number of publications are the USA, England, France and Germany (66.8%), with Turkey
ranking 16th in this area. The main keywords are "ultrasonography, ultrasound, sonography, anatomy,
computed tomography, imaging", indicating that the studies focus particularly on these topics. Our study
shows that there are many researchers active in the field of veterinary anatomy and that the number of
studies in this area is increasing. This bibliometric analysis reveals global trends and significant studies in
the field of veterinary anatomy and ultrasound imaging, providing valuable insights into the future
direction of research in this area.
Downloads
References
H. Brenton, J. Hernandez, F. Bello, P. Strutton, S. Purkayastha, T. Firth, and A. Darzi, “Using multimedia and web 3D to enhance anatomy teaching,” Comput. Educ., vol. 49, pp. 32–53, 2007.
M. Brunel, V. S. Schäfer, and F. Recker, “Bringing anatomy to life: the role of clinical ultrasound in undergraduate medical education – a systematic review,” Ultrasound J., vol. 17, p. 39, 2025. Available: https://doi.org/10.1186/s13089-025-00443-3
D. Yitbarek and G. G. Dagnaw, “Application of advanced imaging modalities in veterinary medicine: a review,” Veterinary Medicine: Research and Reports, pp. 117–130, 2022.
A. Elliott, “Medical imaging,” Nucl. Instrum. Meth. A, vol. 546, pp. 1–13, 2005.
O. Yılmaz, Van Kedilerinde Ön Bacak İskeletinin Bilgisayarlı Tomografi ile Üç Boyutlu Olarak İncelenmesi, Doktora tezi, Van Yüzüncü Yıl Üniversitesi, Sağlık Bilimleri Enstitüsü, Veteriner Fakültesi, Anatomi ABD, Van, Türkiye, 2018.
D. A. Dinç, Ultrason Fiziği ve İneklerde Reprodüktif Ultrasonografi. Konya, Türkiye: Pozitif Matbaacılık Ltd. Şti, 2008.
O. Yılmaz, “Artificial Intelligence-Assisted Veterinary Anatomy: The Role of Digital Transformation in Education and Clinical Practice,” in Uygulamalı Bilimlerde Güncel Çalışmalar-II, A. F. Demirel and O. Yılmaz, Eds., 1st ed. Ankara, Türkiye: IKSAD Publishing, 2024, pp. 3–30.
S. Elasan and O. Yilmaz, “Comprehensive Global Analysis of Future Trends in Artificial Intelligence‐Assisted Veterinary Medicine,” Vet. Med. Sci., vol. 11, no. 3, e70258, 2025.
VOSviewer software, Version 1.6.20, Leiden University, Holland, 2023.
T. G. Nyland and J. S. Mattoon, Small Animal Diagnostic Ultrasound, 3rd ed. Elsevier, 2015.
D. Penninck and M.-A. d’Anjou, Atlas of Small Animal Ultrasonography, 2nd ed. Ames, IA, USA: John Wiley & Sons Inc., 2015.
A. S. Vickram, S. S. Infant, and H. Chopra, “AI-Powered Techniques in Anatomical Imaging: Impacts on Veterinary Diagnostics and Surgery,” Ann. Anat.-Anat. Anzeiger, 152355, 2024.
K. Mandalaneni, P. Venkatapathappa, S. Koshy, G. Walcott-Bedeau, and V. Singh, “Transcranial Doppler Ultrasonography-Related Research in the Caribbean Region,” Cureus, vol. 15, no. 2, e35147, 2023. Available: https://doi.org/10.7759/cureus.35147
Z. Yu, J. Xiao, F. Ma, and S. Li, “Identifying research activity on brain ultrasonography in craniocerebral diseases by bibliometric and visualized analysis of a 20-year journey of global publications,” Medicine, vol. 104, no. 12, e41927, 2025. Available: https://doi.org/10.1097/MD.0000000000041927
Q. Fu, Z. Lu, J. Sui, Y. Chang, and M. Zhang, “Trends in Artificial Intelligence and Ultrasound Medicine: A Bibliometric and Visualized Analysis,” 2023. [Online]. Available: https://ssrn.com/abstract=4333609
S. H. Kim and B. I. Choi, “Three-dimensional and four-dimensional ultrasound: techniques and abdominal applications,” J. Med. Ultrasound, vol. 15, no. 4, pp. 228–242, 2007.
P. Marhofer and V. W. Chan, “Ultrasound-guided regional anesthesia: current concepts and future trends,” Anesth. Analg., vol. 104, no. 5, pp. 1265–1269, 2007.
C. R. Cook, “Ultrasound imaging of the musculoskeletal system,” Vet. Clin. Small Anim. Pract., vol. 46, no. 3, pp. 355–371, 2016.
Z. Wu and Y. Wang, “Development of Guidance Techniques for Regional Anesthesia: Past, Present and Future,” J. Pain Res., vol. 14, pp. 1631–1641, 2021. Available: https://doi.org/10.2147/JPR.S316743
C. H. Wang et al., “Application of Machine Learning to Ultrasonography in Identifying Anatomical Landmarks for Cricothyroidotomy Among Female Adults: A Multi-center Prospective Observational Study,” J. Imaging Inf. Med., vol. 37, no. 1, pp. 363–373, 2024.
R. Lakkadghatwala et al., “Ultrasound guidance compared to anatomic landmark approach for thoracentesis: A systematic review and meta-analysis,” Am. J. Emerg. Med., vol. 97, pp. 159–164, 2025. Available: https://doi.org/10.1016/j.ajem.2025.07.049
D. U. Wilson, M. Q. Bailey, and J. Craig, “The role of artificial intelligence in clinical imaging and workflows,” Vet. Radiol. Ultrasound, vol. 63, pp. 897–902, 2022.
S. Burti et al., “Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations,” Res. Vet. Sci., vol. 175, 105317, 2024. Available: https://doi.org/10.1016/j.rvsc.2024.105317
N. J. Van Eck and L. Waltman, VOSviewer Manual for VOSviewer Version 1.6.20. Leiden, Netherlands: Universiteit Leiden/CWTS, 2022.
M. Aria and C. Cuccurullo, “An R-tool for comprehensive science mapping analysis,” J. Informetrics, vol. 11, no. 4, pp. 959–975, 2017. Available: https://doi.org/10.1016/j.joi.2017.08.007
I. Zupic and T. Čater, “Bibliometric methods in management and organization,” Organ. Res. Methods, vol. 18, no. 3, pp. 429–472, 2015. Available: https://doi.org/10.1177/1094428114562629
