Yuki Kurita

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

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.

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproducible testing. However, constructing high-accuracy deep learning models necessitates a significant amount of manually labeled data, which takes time. To address this issue, we used the Noisy Student Training technique to create a binary classification deep learning model for cervical cytology screening, which reduces the quantity of labeled data necessary. We used 140 whole-slide images from liquid-based cytology specimens, 50 of which were low-grade squamous intraepithelial lesions, 50 were high-grade squamous intraepithelial lesions, and 40 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. Our model efficiently classified normal and abnormal images at low magnification with high reliability, making it a promising screening tool for cervical cytology.