AI-driven paradigm shift in follicle ultrasound monitoring: from automated segmentation to clinical decision support

Abstract

This commentary delineates the developmental pathway of artificial intelligence (AI) in ultrasound follicular monitoring, highlighting a paradigm shift from automated segmentation to clinical decision support. The deep learning-based CR-Unet and C-Rend models have enabled accurate follicle segmentation and measurement from two-dimensional to three-dimensional imaging, substantially improving boundary segmentation accuracy and measurement consistency. Building on this foundation, the study further establishes two-dimensional follicle area and three-dimensional follicle volume as novel biomarkers, providing quantitative criteria for predicting oocyte maturity and optimizing the timing of human chorionic gonadotrophin triggering. Through seamless integration of algorithms into the Acclarix LXK9 ultrasonography equipment, an intelligent monitoring platform with real-time analytical capabilities has been developed, demonstrating significantly superior measurement accuracy and consistency compared with manual operations. These advancements represent a transformative leap from image segmentation to AI-driven clinical decision making, offering robust technical support for standardized and precise management in assisted reproduction.