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Review
. 2025 May 14:11:20552076241310060.
doi: 10.1177/20552076241310060. eCollection 2025 Jan-Dec.

The application of ultrasound artificial intelligence in the diagnosis of endometrial diseases: Current practice and future development

Qiao Wei  1   2   3 , Zhang Xiao  2   3   4 , Xiaowen Liang  1   2 , Zhili Guo  1   2 , Yanfen Zhang  1   5 , Zhiyi Chen  1   2   5
Affiliations
Review

The application of ultrasound artificial intelligence in the diagnosis of endometrial diseases: Current practice and future development

Qiao Wei et al. Digit Health. .

Abstract

Diagnosis and treatment of endometrial diseases are crucial for women's health. Over the past decade, ultrasound has emerged as a non-invasive, safe, and cost-effective imaging tool, significantly contributing to endometrial disease diagnosis and generating extensive datasets. The introduction of artificial intelligence has enabled the application of machine learning and deep learning to extract valuable information from these datasets, enhancing ultrasound diagnostic capabilities. This paper reviews the progress of artificial intelligence in ultrasound image analysis for endometrial diseases, focusing on applications in diagnosis, decision support, and prognosis analysis. We also summarize current research challenges and propose potential solutions and future directions to advance ultrasound artificial intelligence technology in endometrial disease diagnosis, ultimately improving women's health through digital tools.

Keywords: Artificial intelligence; deep learning; disease diagnosis; endometrium; ultrasound images.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Number of AI papers published on ultrasound endometrium per year.
Figure 2.
Figure 2.
Paper screening process.
Figure 3.
Figure 3.
Distribution of types of datasets used in research papers.
Figure 4.
Figure 4.
Endometrial vaginal ultrasound image.
Figure 5.
Figure 5.
3D multi planar ultrasound sonogram of unicornuate uterus.
Figure 6.
Figure 6.
Categories of AI algorithms.
Figure 7.
Figure 7.
Schematic diagram of machine learning algorithm processing flowchart.
Figure 8.
Figure 8.
Categories of prognostic analysis research.
Figure 9.
Figure 9.
Percentage of machine learning applications for endometrial disease diagnosis.
Figure 10.
Figure 10.
Share of major applications of deep learning in endometrial cancer.
Figure 11.
Figure 11.
Deep learning research in endometriosis.
Figure 12.
Figure 12.
Distribution of tolerance assessment studies.
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