AI-Driven Cervical Cancer Cytological Diagnosis Solution based on Large Scale Data Collections and Annotations: A Multi-centre Clinical Validation

Abstract

AbstractCervical cancer is a major health concern for women worldwide, and cervical cytology screening is a widely used and effective technique for early detection. In this study, we built a large-scale database of digital WSIs from 49 hospitals in China, comprising of 76,614 WSIs with 3,435,463 cell-level annotations by 26 cytopathologists using manual and semi-automatic approaches. A novel AI diagnostic system called CCA-DIAG was developed for cervical cancer screening based on a hybrid machine learning framework, which is capable of efficient WSI-level classification for various sedimentations. Our results of multi-center validation show that the system can make classifications at the WSI-level with high sensitivity (ASCUS+:0.89, LSIL+:0.99) for diverse sedimentations and significantly improve the time efficiency of cytopathologists by approximately 4 times. These findings suggest that CCA-DIAG is a promising tool for cervical cancer screening and could potentially improve diagnosis accuracy and efficiency in clinical practice.