TY - JOUR
T1 - AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation
AU - Du, Hui
AU - Dai, Wenkui
AU - Zhou, Qian
AU - Li, Changzhong
AU - Li, Shuai Cheng
AU - Wang, Chun
AU - Tang, Jinlong
AU - Wu, Xiangchen
AU - Wu, Ruifang
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - cervical cancer screening
KW - HPV
KW - low-resource areas
KW - slide interpretation
UR - http://www.scopus.com/inward/record.url?scp=85178890711&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85178890711&origin=recordpage
U2 - 10.3389/fonc.2023.1290112
DO - 10.3389/fonc.2023.1290112
M3 - RGC 21 - Publication in refereed journal
C2 - 38074680
SN - 2234-943X
VL - 13
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 1290112
ER -
