Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 10,656 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide images (WSI). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher uptake of specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

Jue Wang, Yunfang Yu, Yujie Tan, Huan Wan, Nafen Zheng, Zifan He, Luhui Mao, Wei Ren, Kai Chen, Zhen Lin, Gui He, Yongjian Chen, Ruichao Chen, Hui Xu, Kai Liu, Qinyue Yao, Sha Fu, Yang Song, Qingyu Chen, Lina Zhu, Liya Wei, Jin Wang, Nengtai Ouyang, Herui Yao