This multicenter study evaluated the analytical performance of an AI-based platform for automated ovarian follicle measurement and count estimation during ovarian stimulation in IVF cycles. Follicle diameters and counts derived from two-dimensional ultrasound images were compared with assessments performed by certified sonographers. The results demonstrated high agreement between the AI platform and expert clinicians, supporting the accuracy and reliability of AI-assisted follicle monitoring. The findings highlight the potential of artificial intelligence to standardise ultrasound assessments, reduce observer-dependent variability, and improve efficiency in IVF monitoring workflows.