Wei Qiao, Xiao Zhang, Liang Xiaowen, Guo Zhili, Zhang Yanfen, Chen Zhiyi
Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China.
Digit Health. 2025 May 14;11:20552076241310060. doi: 10.1177/20552076241310060. eCollection 2025 Jan-Dec.
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.
子宫内膜疾病的诊断和治疗对女性健康至关重要。在过去十年中，超声已成为一种无创、安全且经济高效的成像工具，对子宫内膜疾病的诊断做出了重大贡献，并产生了大量数据集。人工智能的引入使得机器学习和深度学习能够应用于从这些数据集中提取有价值的信息，从而提高了超声诊断能力。本文综述了人工智能在子宫内膜疾病超声图像分析中的进展，重点介绍其在诊断、决策支持和预后分析中的应用。我们还总结了当前的研究挑战，并提出了潜在的解决方案和未来方向，以推动超声人工智能技术在子宫内膜疾病诊断中的发展，最终通过数字工具改善女性健康。
