Towards Automated Placental Screening: Instance Segmentation in Clinical Images

While routine macroscopic placental assessment is performed in all deliveries, anatomopathological examination is reserved for selected cases due to logistical and resource constraints. Although artificial intelligence (AI) has shown promise in medical imaging, its application to postpartum placental screening remains underexplored in clinical settings. This study addresses this gap by developing a deep learning pipeline to improve segmentation of placental structures using macroscopic photographs captured in delivery rooms, with the aim of supporting anatomopathological referral decisions. A proprietary dataset was constructed under both standardized and routine clinical conditions, with annotations curated by pathologists across eight morphological classes. Three architectures—YOLOv11, U-Net with ResNet34, and U-Net with EfficientNet-B0—were evaluated for segmentation and classification performance. Results demonstrated moderate efficacy: YOLOv11 achieved an AP50 of 72% for instance segmentation, while U-Net variants showed higher classification accuracy (up to 85.18%) but lower segmentation precision (Dice scores: 24.70–61.97%). These findings support the feasibility of incorporating AI-based photographic analysis into routine clinical workflows for placental evaluation. Despite preliminary segmentation limitations, this work establishes a foundation for AI-driven placental screening, offering potential to optimize resource allocation by guiding anatomopathological referrals.