Endometriosis is a gynecologic inflammatory condition that affects up to 10% of reproductive-aged women worldwide. The disease exhibits heterogeneous presentations and is associated with a prolonged diagnostic delay, often exceeding seven years, because existing diagnostic modalities such as transvaginal ultrasound, magnetic resonance imaging, and the biomarker cancer antigen 125 (CA-125) are suboptimal. This review examines how machine learning (ML) is playing an increasingly significant role in early, non-surgical endometriosis diagnosis through two main approaches: symptom clustering and imaging integration. Unsupervised ML algorithms such as k-means, partitioning around medoids, and Bayesian networks have demonstrated success in identifying clinically informative endometriosis phenotypes from patient-reported symptoms and electronic health records. Concurrently, ML models such as convolutional neural networks and radiomics approaches have achieved high accuracy in lesion detection from imaging data, in some cases surpassing human interpretation. Despite these advances, significant challenges remain, including limited access to large, annotated multimodal datasets, the absence of widely accepted evaluation standards, and concerns regarding interpretability and generalizability. Multicenter, integrative studies and the incorporation of explainability techniques are recommended as potential strategies to address these gaps. Finally, multimodal ML approaches that combine symptomatology and imaging data hold substantial promise for reducing diagnostic delays, facilitating early intervention, and improving clinical outcomes in the management of endometriosis.
Ramadan et al. (2025) studied this question.