AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation

Hui Du; Wenkui Dai; Qian Zhou; Changzhong Li; Shuai Cheng Li; Chun Wang; Jinlong Tang; Xiangchen Wu; Ruifang Wu

doi:10.3389/fonc.2023.1290112

AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation

Front Oncol. 2023 Nov 23:13:1290112. doi: 10.3389/fonc.2023.1290112. eCollection 2023.

Authors

Hui Du^#^{1

2

3}, Wenkui Dai^#^{1

2

3}, Qian Zhou^#⁴, Changzhong Li^#⁵, Shuai Cheng Li⁴, Chun Wang^{1

2

3}, Jinlong Tang^{1

2

3}, Xiangchen Wu⁵, Ruifang Wu^{1

2

3}

Affiliations

¹ Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, China.
² Institute of Obstetrics and Gynecology, Shenzhen Peking University-The Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, Shenzhen, China.
³ Shenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases, Shenzhen, China.
⁴ Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
⁵ Suzhou Ruiqian Technology Company Ltd., Suzhou, China.

^# Contributed equally.

Abstract

Given the shortage of cytologists, women in low-resource regions had inequitable access to cervical cytology which plays an pivotal role in cervical cancer screening. Emerging studies indicated the potential of AI-assisted system in promoting the implementation of cytology in resource-limited settings. However, there is a deficiency in evaluating the aid of AI in the improvement of cytologists' work efficiency. This study aimed to evaluate the feasibility of AI in excluding cytology-negative slides and improve the efficiency of slide interpretation. Well-annotated slides were included to develop the classification model that was applied to classify slides in the validation group. Nearly 70% of validation slides were reported as negative by the AI system, and none of these slides were diagnosed as high-grade lesions by expert cytologists. With the aid of AI system, the average of interpretation time for each slide decreased from 3 minutes to 30 seconds. These findings suggested the potential of AI-assisted system in accelerating slide interpretation in the large-scale cervical cancer screening.

Keywords: HPV; artificial intelligence; cervical cancer screening; low-resource areas; slide interpretation.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by Shenzhen Science and Technology Project (GJHZ20210705142543018).