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The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women

BACKGROUND: Adequate cytology is limited by insufficient cytologists in a large‐scale cervical cancer screening. We aimed to develop an artificial intelligence (AI)‐assisted cytology system in cervical cancer screening program. METHODS: We conducted a perspective cohort study within a population‐bas...

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Autores principales: Bao, Heling, Sun, Xiaorong, Zhang, Yi, Pang, Baochuan, Li, Hua, Zhou, Liang, Wu, Fengpin, Cao, Dehua, Wang, Jian, Turic, Bojana, Wang, Linhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520355/
https://www.ncbi.nlm.nih.gov/pubmed/32697872
http://dx.doi.org/10.1002/cam4.3296
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author Bao, Heling
Sun, Xiaorong
Zhang, Yi
Pang, Baochuan
Li, Hua
Zhou, Liang
Wu, Fengpin
Cao, Dehua
Wang, Jian
Turic, Bojana
Wang, Linhong
author_facet Bao, Heling
Sun, Xiaorong
Zhang, Yi
Pang, Baochuan
Li, Hua
Zhou, Liang
Wu, Fengpin
Cao, Dehua
Wang, Jian
Turic, Bojana
Wang, Linhong
author_sort Bao, Heling
collection PubMed
description BACKGROUND: Adequate cytology is limited by insufficient cytologists in a large‐scale cervical cancer screening. We aimed to develop an artificial intelligence (AI)‐assisted cytology system in cervical cancer screening program. METHODS: We conducted a perspective cohort study within a population‐based cervical cancer screening program for 0.7 million women, using a validated AI‐assisted cytology system. For comparison, cytologists examined all slides classified by AI as abnormal and a randomly selected 10% of normal slides. Each woman with slides classified as abnormal by either AI‐assisted or manual reading was diagnosed by colposcopy and biopsy. The outcomes were histologically confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+). RESULTS: Finally, we recruited 703 103 women, of whom 98 549 were independently screened by AI and manual reading. The overall agreement rate between AI and manual reading was 94.7% (95% confidential interval [CI], 94.5%‐94.8%), and kappa was 0.92 (0.91‐0.92). The detection rates of CIN2+ increased with the severity of cytology abnormality performed by both AI and manual reading (P (trend) < 0.001). General estimated equations showed that detection of CIN2+ among women with ASC‐H or HSIL by AI were significantly higher than corresponding groups classified by cytologists (for ASC‐H: odds ratio [OR] = 1.22, 95%CI 1.11‐1.34, P < .001; for HSIL: OR = 1.41, 1.28‐1.55, P < .001). AI‐assisted cytology was 5.8% (3.0%‐8.6%) more sensitive for detection of CIN2+ than manual reading with a slight reduction in specificity. CONCLUSIONS: AI‐assisted cytology system could exclude most of normal cytology, and improve sensitivity with clinically equivalent specificity for detection of CIN2+ compared with manual cytology reading. Overall, the results support AI‐based cytology system for the primary cervical cancer screening in large‐scale population.
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spelling pubmed-75203552020-09-30 The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women Bao, Heling Sun, Xiaorong Zhang, Yi Pang, Baochuan Li, Hua Zhou, Liang Wu, Fengpin Cao, Dehua Wang, Jian Turic, Bojana Wang, Linhong Cancer Med Cancer Prevention BACKGROUND: Adequate cytology is limited by insufficient cytologists in a large‐scale cervical cancer screening. We aimed to develop an artificial intelligence (AI)‐assisted cytology system in cervical cancer screening program. METHODS: We conducted a perspective cohort study within a population‐based cervical cancer screening program for 0.7 million women, using a validated AI‐assisted cytology system. For comparison, cytologists examined all slides classified by AI as abnormal and a randomly selected 10% of normal slides. Each woman with slides classified as abnormal by either AI‐assisted or manual reading was diagnosed by colposcopy and biopsy. The outcomes were histologically confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+). RESULTS: Finally, we recruited 703 103 women, of whom 98 549 were independently screened by AI and manual reading. The overall agreement rate between AI and manual reading was 94.7% (95% confidential interval [CI], 94.5%‐94.8%), and kappa was 0.92 (0.91‐0.92). The detection rates of CIN2+ increased with the severity of cytology abnormality performed by both AI and manual reading (P (trend) < 0.001). General estimated equations showed that detection of CIN2+ among women with ASC‐H or HSIL by AI were significantly higher than corresponding groups classified by cytologists (for ASC‐H: odds ratio [OR] = 1.22, 95%CI 1.11‐1.34, P < .001; for HSIL: OR = 1.41, 1.28‐1.55, P < .001). AI‐assisted cytology was 5.8% (3.0%‐8.6%) more sensitive for detection of CIN2+ than manual reading with a slight reduction in specificity. CONCLUSIONS: AI‐assisted cytology system could exclude most of normal cytology, and improve sensitivity with clinically equivalent specificity for detection of CIN2+ compared with manual cytology reading. Overall, the results support AI‐based cytology system for the primary cervical cancer screening in large‐scale population. John Wiley and Sons Inc. 2020-07-22 /pmc/articles/PMC7520355/ /pubmed/32697872 http://dx.doi.org/10.1002/cam4.3296 Text en © 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Prevention
Bao, Heling
Sun, Xiaorong
Zhang, Yi
Pang, Baochuan
Li, Hua
Zhou, Liang
Wu, Fengpin
Cao, Dehua
Wang, Jian
Turic, Bojana
Wang, Linhong
The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women
title The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women
title_full The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women
title_fullStr The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women
title_full_unstemmed The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women
title_short The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women
title_sort artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: a population‐based cohort study of 0.7 million women
topic Cancer Prevention
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520355/
https://www.ncbi.nlm.nih.gov/pubmed/32697872
http://dx.doi.org/10.1002/cam4.3296
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