Ultrasound radiomics and deep learning for predicting antral follicle count and anti-Mullerian hormone.

ABSTRACT: To overcome inter-observer variability in conventional antral follicle count (AFC) assessment and AMH testing limitations, we developed an AI-powered framework using routine 2D ultrasound to standardize ovarian-reserve evaluation in assisted reproductive technology (ART). This multicenter retrospective study analyzed 395 women with infertility from two affiliated hospitals. The cohort was divided into training (n = 210), internal-test (n = 91), and external-test (n = 94) cohorts. We established three prediction models: radiomics model, 674 IBSI-compliant features; deep-learning model, ResNet50-based feature extraction; fusion model, hybrid approach combining both modalities. Model performance was validated against the manual AFC and serum AMH levels. Sequential classification categorized p

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12830614 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature