Whole Slide Imaging-Free Supporting Tool for Cytotechnologists in Cervical Cytology

Abstract

Cervical cytology is a crucial method for detecting cancerous and precancerous lesions. However, traditional workflows rely heavily on manual microscopic observations by cytotechnologists, making the process time-consuming and labor-intensive. Although several artificial intelligence (AI)-assisted cytology systems have been developed, most approaches require whole slide images, which entails costly scanning equipment, extensive data storage, and additional processing time. These factors hinder real-time diagnosis and are often impractical in resource-limited settings. In this study, we developed cytology-all-in-one (CYTOLONE), a novel AI model designed to support cytotechnologists in cervical cytology. CYTOLONE was constructed using a model based on OpenAI’s contrastive language-image pretraining framework and fine-tuned using a hierarchical labeling structure. This approach enabled the model to effectively learn the relationship between low-magnification images and cytologic features. By integrating the microscope directly with an Apple Silicon Mac and using an iPhone camera for image capture, CYTOLONE offers real-time evaluation, processing each image in under 0.5 seconds. The evaluation results demonstrated that CYTOLONE achieved superior classification accuracy compared with both contrastive language-image pretraining-ViT-B/16 and GynAIe-B16-10k. The model maintained high Anomaly detection accuracy (95.8%) and significantly improved accuracy in Malignancy (92.8%), Bethesda (61.5%), and Diagnosis (57.5%) categories. Furthermore, feature space visualization revealed clearer boundaries between diagnostic categories, reflecting CYTOLONE’s improved performance. The proposed workflow seamlessly integrates traditional cytology practices, allowing cytotechnologists to receive AI support during real-time specimen observation. This innovative workflow eliminates the need for costly whole slide image scanners, improves diagnostic efficiency, and is well-suited for resource-limited environments. Our findings suggest that CYTOLONE can enhance the efficiency of cytotechnologists and improve diagnostic accuracy, offering a practical solution to the existing limitations in AI-assisted cytology systems.