Feasibility multi-center study of artificial intelligence assistance in cervical fluid-based cytology diagnosis

Abstract

Objective: To propose a method of cervical cytology screening based on deep convolutional neural network and compare it with the diagnosis of cytologists. Method: The deep segmentation network was used to extract 618 333 regions of interest (ROI) from 5, 516 cytological pathological images. Combined with the experience of physicians, the deep classification network with the ability to analyze ROI was trained. The classification results were used to construct features, and the decision model was used to complete the classification of cytopathological images. Results: The sensitivity and specificity were 89.72%, 58.48%, 33.95% and 95.94% respectively. Among the smears derived from four different preparation methods, this algorithm had the best effect on natural fallout with a sensitivity of 91.10%, specificity of 69.32%, positive predictive rate of 41.41%, and negative predictive rate of 97.03%. Conclusion: Deep convolutional neural network image recognition technology can be applied to cervical cytology screening.