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

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 Zuo, Liya Wei, Jin Wang (), Nengtai Ouyang () and Herui Yao ()
Additional contact information
Jue Wang: Sun Yat-sen University
Yunfang Yu: Sun Yat-sen University
Yujie Tan: Sun Yat-sen University
Huan Wan: Sun Yat-sen University
Nafen Zheng: Sun Yat-sen University
Zifan He: Sun Yat-sen University
Luhui Mao: Sun Yat-sen University
Wei Ren: Sun Yat-sen University
Kai Chen: Sun Yat-sen University
Zhen Lin: Cells Vision (Guangzhou) Medical Technology Inc.
Gui He: Sun Yat-sen University
Yongjian Chen: Center for Molecular Medicine, Karolinska Institutet
Ruichao Chen: The Third Affiliated Hospital of Guangzhou Medical University
Hui Xu: Guangzhou Medical University
Kai Liu: Cells Vision (Guangzhou) Medical Technology Inc.
Qinyue Yao: Cells Vision (Guangzhou) Medical Technology Inc.
Sha Fu: Sun Yat-sen University
Yang Song: Sun Yat-sen University
Qingyu Chen: Sun Yat-sen University
Lina Zuo: Sun Yat-sen University
Liya Wei: Sun Yat-sen University
Jin Wang: Cells Vision (Guangzhou) Medical Technology Inc.
Nengtai Ouyang: Sun Yat-sen University
Herui Yao: Sun Yat-sen University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract 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 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). 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 AUC, 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.

Date: 2024
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DOI: 10.1038/s41467-024-48705-3

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