Development of an AI-based magnetic resonance imaging reading support program (AMP) for deep endometriosis diagnosis.

Diagnosis of endometriosis faces significant challenges including diagnostic delay and reliance on invasive procedures. Deep endometriosis (DE) poses additional difficulties in non-invasive diagnosis due to its subtle and complex imaging features. To address these challenges, we developed an AI-based MRI reading support program (AMP) designed to improve diagnostic accuracy and efficiency, with the primary endpoint of demonstrating its potential to enhance radiologists' reading sensitivity. AMP comprises the following three models: (1) a nnU-Net model for endometriotic nodular lesion (plaque) segmentation, (2) a radiomics-based LightGBM model for adhesion detection, and (3) a nnU-Net model for detection/quantification of ovarian endometriotic cysts (OECs). In cross-validation, AMP achieves mean Dice similarity coefficient of 0.293 for plaque segmentation and 0.580 for OEC segmentation. For adhesion detection, AMP shows high performance for uterine adhesions (F1 scores > 0.6). In a preliminary clinical utility study with three radiologists, AMP improved mean recall for plaque detection from 0.73 to 0.91 demonstrating AMP's ability to support radiologists in identifying subtle DE lesions and adhesions. Our findings show that AMP is a reliable non-invasive clinical diagnosis tool, that has the potential to minimize diagnostic delays and improve patient outcome.