Abstract
BACKGROUND
Gestational diabetes mellitus (GDM) poses a significant threat to maternal and fetal health, and placental pathology is crucial for assessing disease severity. Traditional diagnosis relies on clinicians' visual assessment, which is often inefficient and subjective.
METHODS
To address these issues, we developed a MONAI-based image augmentation strategy and integrated the GAMatrix attention module within the YOLOv8 framework. Our study used a dataset of 5,065 placental pathology images (80% for training and 20% for validation) covering five pathology categories, supplemented by an external validation set of 500 images. A MONAI-based image augmentation pipeline was used to increase training-image diversity.
RESULTS
Four model configurations were compared: YOLOv8 trained on the original dataset, YOLOv8 trained with MONAI-based augmentation, GAMatrix-YOLOv8 trained on the original dataset, and GAMatrix-YOLOv8 trained with MONAI-based augmentation. In the original comparative analysis, YOLOv8 trained on the original dataset achieved a macro-F1 score of 95.56% (95% CI, 94.86%-96.26%) in internal validation. The GAMatrix-YOLOv8 model trained with MONAI-based augmentation showed the strongest overall performance among the evaluated configurations, while external image-level evaluation indicated persistent class-specific errors, particularly for thrombotic vascular lesion and chorangiosis.
CONCLUSION
These findings suggest that attention-enhanced YOLOv8 combined with MONAI-based augmentation may support placental histopathological image classification, but the results should be interpreted as image-level findings and require patient-level, multicenter, and prospective validation.