Development and Clinical Application of a Deep Learning-Based AI Support Model for Endometrial Cancer Cytology

The global increase in endometrial cancer, including in Japan, and a shortage of pathologists and cytotechnologists have increased the diagnostic burden, emphasizing the need for an AI-based diagnostic support model that uses deep learning. We evaluated the clinical application of an improved AI-supported endometrial cytology model.

Using YOLOv5x and YOLOv7 models evaluated by mean average precision (mAP), we compared two datasets-one annotated for both benign and malignant cell clusters, and one for malignant only. In addition, using the Two One-Sided Tests (TOST) procedure, we assessed the correlation between AI diagnostic accuracy and the level of difficulty perceived by human diagnosticians. Finally, we used Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize and enhance the interpretability of the AI model's decision-making process.

The YOLOv5x model with both benign and malignant annotations had the highest malignant mAP, 0.798, as compared with YOLOv7. The TOST analysis showed no significant difference in perceived diagnostic difficulty between cases that were correctly and incorrectly diagnosed by the AI model, indicating consistent AI accuracy regardless of case difficulty. Grad-CAM visualizations clarified the AI model's decision-making basis; in some cases, the model appeared to focus on regions that differed from those typically attended to by human diagnosticians.

The AI support model showed high and consistent accuracy in endometrial cytological analysis, regardless of diagnostic difficulty as perceived by human diagnosticians. Grad-CAM visualizations revealed diagnostic patterns, and the AI occasionally focused on regions different from those emphasized by human diagnosticians. This study advanced a real-time microscope-integrated AI system toward clinical application.