人工智能辅助分析在宫颈液基薄层细胞学检查中的应用价值

doi:10.3969/j.issn.1006-7795.2020.03.008

首都医科大学学报 ›› 2020, Vol. 41 ›› Issue (3): 360-363.doi: 10.3969/j.issn.1006-7795.2020.03.008

人工智能辅助分析在宫颈液基薄层细胞学检查中的应用价值

李雪¹, 石中月¹, 杨志明², 庞文博², 金木兰¹

1. 首都医科大学附属北京朝阳医院病理科, 北京 100020;
2. 深思考人工智能机器人科技(北京)有限公司, 北京 100085

收稿日期:2020-02-10 出版日期:2020-06-21 发布日期:2020-06-17
通讯作者: 金木兰 E-mail:kinmokuran@163.com
基金资助:
北京市自然科学基金（7202056）。

Value about artificial intelligence-assisted liquid-based thin-layer cytology for cytology cervical cancer screening

Li Xue¹, Shi Zhongyue¹, Yang Zhiming², Pang Wenbo², Jin Mulan¹

1. Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China;
2. iDeepwise on Artificial Intelligence Robot Technology(Beijing) Co, Ltd, Beijing 100085, China

Received:2020-02-10 Online:2020-06-21 Published:2020-06-17
Supported by:
This study was supported by Natural Science Foundation of Beijing(7202056).

摘要/Abstract

摘要： 目的探讨人工智能辅助分析在宫颈液基薄层细胞学检查中的应用价值。方法选取首都医科大学附属北京朝阳医院病理科1 000例存档的液基薄层宫颈细胞涂片作为标准样例，通过深思考人工智能机器人科技（北京）有限公司研发的人工智能系统辅助检查和专业病理医师人工检查两种方式进行多组对比实验，对1 000例涂片的检查结果进行对比，以TBS2014的阳性分级准则，将意义不明的非典型鳞状细胞（atypical squamous cells of undetermined significance，ASC-US）及以上级别作为宫颈癌前病变的阳性标准，统计其灵敏度与特异度等指标，并分析不同检查方式结果的差异。结果人工智能辅助检查和专业病理医师人工检查的结果与病理科存档的判断结果基本一致。人工智能辅助筛查的灵敏度、特异度和准确率分别为100.00%、90.68%和97.80%。结论在人工智能辅助检查基础上结合病理医生的阅片技巧，可有效地减少漏诊的发生，具有高灵敏度与特异度，并能大大减轻医生的工作量。

关键词: 宫颈细胞学, 宫颈癌筛查, 人工智能辅助分析

Abstract: Objective To explore the value of artificial intelligence-assistance for cytology cervical cancer screening in liquid-based cytology. Method Totally 1000 liquid-based cytology cervical cell smears archived in Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University were selected. Multiple groups of comparative experiment were designed through artificial intelligence-assisted screening system developed by iDeepWise Company and professional pathologists manual diagnosis. The dysplasia classified into atypical squamous cells of undetermined significance (ASC-US) or higher grade was regarded as the positive criteria for cervical precancerous lesions based on the positive grading criteria of TBS 2014. The differences in the results of screening methods were analyzed, and the sensitivity and specificity were calculated. Results The results of artificial intelligence-assisted screening and manual diagnosis by professional pathologists were basically consistent with the previously archived results. The sensitivity, specificity, and accuracy of artificial intelligence-assisted screening were 100.00%,90.68% and 97.80%, respectively. Conclusion The artificial intelligence-assisted screening combined with the pathologist's reading skills could effectively reduce the incidence of missed diagnosis, with high sensitivity and specificity. It also could greatly reduce the workload of pathologists.

Key words: cervical cytology, cervical cancer screening, artificial intelligence-assisted analysis

中图分类号:

李雪, 石中月, 杨志明, 庞文博, 金木兰. 人工智能辅助分析在宫颈液基薄层细胞学检查中的应用价值[J]. 首都医科大学学报, 2020, 41(3): 360-363.

Li Xue, Shi Zhongyue, Yang Zhiming, Pang Wenbo, Jin Mulan. Value about artificial intelligence-assisted liquid-based thin-layer cytology for cytology cervical cancer screening[J]. Journal of Capital Medical University, 2020, 41(3): 360-363.

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人工智能辅助分析在宫颈液基薄层细胞学检查中的应用价值

Value about artificial intelligence-assisted liquid-based thin-layer cytology for cytology cervical cancer screening

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