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

Author(s)

Hui Du
Wenkui Dai
Changzhong Li
Chun Wang
Jinlong Tang
Xiangchen Wu
Ruifang Wu

Related Research Unit(s)

Department of Computer Science

Detail(s)

Original language	English
Article number	1290112
Journal / Publication	Frontiers in Oncology
Volume	13
Online published	23 Nov 2023
Publication status	Published - 2023

Link(s)

DOI	DOI https://doi.org/10.3389/fonc.2023.1290112 Final Published version
	Check@CityULib
Attachment(s)	Documents 180184300.pdf Final Published version, 7.35 MB, PDF Publisher's Copyright Statement This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-85178890711&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(29c2e6aa-94f8-43f0-95ff-c85c99f476bd).html

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. Copyright © 2023 Du, Dai, Zhou, Li, Li, Wang, Tang, Wu and Wu.