Illuminating Research Dynamics: Medical Ultrasound and Deep Learning

Clinical Snapshot

Key Findings

• Effect Size: Not applicable (bibliometric study)

• Primary Outcome: 3386 publications identified with 31.02% annual growth rate in deep learning ultrasound research

• Nnt Or Sensitivity: China leads with 1355 publications; UK highest citation rate at 55.5 citations per article

• Confidence Interval: Not reported

Clinical Application

High feasibility for research planning and collaboration identification Limited direct relevance to Australian clinical practice. May inform research funding priorities and international collaboration opportunities for Australian institutions. Researchers and clinicians interested in deep learning applications in medical ultrasound

Abstract

BACKGROUND: Deep learning (DL) has enabled advances in ultrasound imaging, but challenges like limited datasets and device variability hinder progress. This study provides the first bibliometric overview of DL research in medical ultrasound. METHOD: We retrieved related publications (2004-Apr 2025) on medical ultrasound and DL from the Web of Science Core Collection. Bibliometric tools (Bibliometrix, VOSviewer, CiteSpace) were used to quantify publication growth, country/institution contributions, and thematic evolution. RESULTS: A total of 3386 publications were included, with the annual output increasing steadily at an average growth rate of 31.02%. While China led all countries in publication volume (1355), the United Kingdom achieved the highest citation rate per article (55.5 citations). Fudan University was the most prolific institution (110), and key authors such as Luca Saba and Jasjit S. Suri formed the principal collaboration hubs. In terms of venues, Diagnostics published the most papers, whereas IEEE Transactions on Medical Imaging exerted the greatest citation impact (5646 citations; IF 8.9). Keyword analysis revealed core themes in classification, segmentation, and CNN-based diagnosis. From 2020 onward, ultrasound-specific topics (e.g., diagnosis, images) dominate; advanced architectures (e.g., U-Net) and emerging clinical concepts such as noninvasive assessment. CONCLUSION: This mapping highlights a rapidly growing, internationally collaborative field. Emerging trends include advanced artificial intelligence (AI) architectures (U-Net, attention-based networks, vision Transformers) and a shift toward surgical and therapeutic applications. These insights underscore the need for multicenter validation, standardized data protocols, and clinically interpretable AI models to accelerate safe translation into practice.

References

1. 1.Qin, H., Zhang, Y., Zhou, J., Han, Q., & Sun, Y. (2026). Illuminating Research Dynamics: Medical Ultrasound and Deep Learning. Journal of Clinical Ultrasound, Article jcu.70131. https://doi.org/10.1002/jcu.70131