From Microscope to AI: Developing an Integrated Diagnostic System for Endometrial Cytology

Abstract

Objective To explore the integration of artificial intelligence (AI)-assisted diagnostics into a cytology workflow, focusing on real-time detection of abnormal cell clusters in endometrial cytology without relying on whole-slide imaging (WSI), utilizing a YOLOv5x-based model. Methods We employed the YOLOv5x object detection model pretrained on the COCO dataset because of its high-speed and accurate detection capabilities. This study involved real-time direct detection of abnormal cell clusters using a CCD camera attached to a microscope, with the aim of enhancing diagnostic efficiency and accuracy in endometrial cytology. The model was further refined through transfer learning using actual cytology case images, emphasizing the need for a delicate balance between technological advancement and clinical integration. Results The integration of our AI model into the diagnostic workflow significantly reduced the time required for diagnosis compared to traditional methods, as demonstrated by the performance metrics that matched or exceeded those of pathologists. This breakthrough underscores the potential of AI to improve diagnostic workflows, particularly in settings where resources or pathology services are limited. Conclusion This study presents the first instance of an AI-assisted system for endometrial cytology that operates in real time under a microscope, negating the need for WSI. Our findings highlight the feasibility of embedding AI directly into existing clinical practices, offering significant time savings and potentially matching the diagnostic accuracy of specialists. The successful integration of this technology is a critical step forward in the application of AI in the medical field, paving the way for broader adoption and further research into user-friendly AI applications in pathology diagnostics.