A Review on the Integration of Artificial Intelligence and Medical Imaging in IVF Ovarian Stimulation

TL;DR

This review examines how AI combined with medical imaging can improve ovarian stimulation in IVF, highlighting current limitations and future opportunities for advanced image analysis and explainable AI to enhance clinical decision-making.

Contribution

It provides a comprehensive analysis of existing studies, identifying gaps such as limited imaging modalities and lack of explainability, and suggests directions for future research in AI-driven IVF treatment.

Findings

AI shows potential in predicting hormonal dosages and outcomes.

Most studies used 2D ultrasound with basic quantifications.

Limited use of advanced image analysis and explainable AI.

Abstract

Artificial intelligence (AI) has emerged as a powerful tool to enhance decision-making and optimize treatment protocols in in vitro fertilization (IVF). In particular, AI shows significant promise in supporting decision-making during the ovarian stimulation phase of the IVF process. This review evaluates studies focused on the applications of AI combined with medical imaging in ovarian stimulation, examining methodologies, outcomes, and current limitations. Our analysis of 13 studies on this topic reveals that, reveal that while AI algorithms demonstrated notable potential in predicting optimal hormonal dosages, trigger timing, and oocyte retrieval outcomes, the medical imaging data utilized predominantly came from two-dimensional (2D) ultrasound which mainly involved basic quantifications, such as follicle size and number, with limited use of direct feature extraction or advanced image analysis techniques. This points to an underexplored opportunity where advanced image analysis approaches, such as deep learning, and more diverse imaging modalities, like three-dimensional (3D) ultrasound, could unlock deeper insights. Additionally, the lack of explainable AI (XAI) in most studies raises concerns about the transparency and traceability of AI-driven decisions - key factors for clinical adoption and trust. Furthermore, many studies relied on single-center designs and small datasets, which limit the generalizability of their findings. This review highlights the need for integrating advanced imaging analysis techniques with explainable AI methodologies, as well as the importance of leveraging multicenter collaborations and larger datasets. Addressing these gaps has the potential to enhance ovarian stimulation management, paving the way for efficient, personalized, and data-driven treatment pathways that improve IVF outcomes.