Research progress on the application of artificial intelligence in controlled ovarian stimulation
10.3760/cma.j.cn101441-20241009-00364
- VernacularTitle:人工智能在辅助生殖控制性卵巢刺激应用的研究进展
- Author:
Jieru ZHU
1
;
Jianping OU
1
Author Information
1. 中山大学附属第三医院生殖医学中心，广州 510630
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Machine learning;
Reproductive technology, assisted;
Controlled ovarian stimulation;
Infertility
- From:
Chinese Journal of Reproduction and Contraception
2025;45(1):14-18
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, artificial intelligence (AI) technology has seen widespread application in the field of healthcare, particularly revolutionizing disease diagnosis and treatment decisions. Assisted reproductive technology (ART), a crucial method for treating infertility, has also benefited from the integration of AI, especially in the intelligent development of its core process--controlled ovarian stimulation (COS). Traditional COS protocols heavily relied on the experience and subjective judgment of physicians, leading to uncertainties. However, AI technology leverages deep learning to analyze multi-dimensional data, including patients' demographic characteristics, reproductive endocrine levels, and ultrasound monitoring results, to provide precise, personalized optimization and dynamic adjustments for COS. Specifically, AI models can accurately calculate the initial COS dosage, intelligently monitor follicular development, and predict the optimal timing for ovulation triggering in real-time, significantly enhancing diagnostic and treatment efficiency, reducing the workload of physicians, and offering more individualized and precise treatment plans for patients. This article reviews the latest research progress in AI applications for individualized optimization of initial gonadotropin dosage during COS, intelligent follicular monitoring, assessment of ovarian responsiveness, and prediction of the optimal timing for ovulation triggering, aiming to provide valuable insights for the clinical practice of AI in assisted reproductive hyperstimulation.
