Progress and Challenge of Artificial Intelligence in Diagnosis and Treatment of Cervical Lesions

doi:10.12114/j.issn.1007-9572.2022.0095

Chinese General Practice ›› 2022, Vol. 25 ›› Issue (18): 2215-2222.DOI: 10.12114/j.issn.1007-9572.2022.0095

Special Issue: 数智医疗最新文章合辑

• Article·Cervical Cancer Study • Previous Articles Next Articles

Progress and Challenge of Artificial Intelligence in Diagnosis and Treatment of Cervical Lesions

1. Affiliated Tumor Hospital of Xinjiang Medical University/State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi 830011, China
2. School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China

Received:2022-01-25 Revised:2022-03-01 Published:2022-06-20 Online:2022-04-20
Contact: ·Rezhake REMILA
About author:
WU A Y, REMILA R, QIAO Y L. Progressand challenge of artificial intelligencein diagnosis and treatment of cervical lesions[J]. Chinese General Practice, 2022, 25 (18) : 2215-2222, 2230.

人工智能在宫颈病变诊断及治疗中的应用进展与挑战

1.830011　新疆维吾尔自治区乌鲁木齐市，新疆医科大学附属肿瘤医院　省部共建中亚高发病成因与防治国家重点实验室
2.100730　北京市，中国医学科学院北京协和医学院群医学及公共卫生学院

通讯作者: 热米拉·热扎克
作者简介:
武爱媛，热米拉·热扎克，乔友林.人工智能在宫颈病变诊断及治疗中的应用进展与挑战[J].中国全科医学，2022，25（18）：2215-2222，2230. [www.chinagp.net] 作者贡献：武爱媛进行文章的构思与设计，提出主要分析指标，负责检索文献、撰写论文；热米拉·热扎克进行论文的修订、质量控制及审校；乔友林对文章整体负责，监督管理。
基金资助:
省部共建中亚高发病成因与防治国家重点实验室开放课题资助项目(SKL-HIDCA-2020-GJ2); 新疆医科大学研究生创新创业项目

Abstract

Abstract:

China has the considerable disease burden of cervical cancer, with the mortality and morbidity of cervical cancer showing an increasing and younger trend. Facing to the critical situation of cervical cancer control, it is urgent to explore the new methods that suitable for different resource areas for the early detection and treatment of cervical cancer. Recently, great progress has been made in the field of AI image classification, and scientists have developed many algorithms to identify cervical lesions and conducted corresponding studies on their accuracy. Here, by reviewing the papers published at home and aboard, which studied the applications value of AI in cervical cytology screening, colposcopy examination, diagnosis and treatment of cervical cancer, we summarized and discussed the current progress and challenges for AI's application in the area of cervical cancer control, in order to provide solid evidence for the future use of AI in improving human health.

Key words: Uterine cervical diseases, Artificial intelligence, Cytological techniques, Cervical cytology, Colposcopy, Screening, Diagnosis, differential, Treatment

摘要：

我国宫颈癌的疾病负担较重，其发病率和死亡率呈逐年升高且年轻化趋势，防控形势较为严峻，急需探索适宜我国不同资源地区的、新型的早诊早治手段，以加快我国宫颈癌的防治步伐。近些年人工智能（AI）在图像分类领域取得较大进展，科学家们开发出众多能够识别宫颈病变的算法，并对其准确性进行了相应的研究。本文结合国内外研究成果，就AI在宫颈细胞学筛查、阴道镜、宫颈恶性肿瘤的诊断及治疗预测过程等方面的应用进展进行综述，并探讨AI在宫颈病变诊断及治疗中遇到的挑战，使AI协助医疗工作者更好地为人类健康保驾护航。

关键词: 宫颈疾病, 人工智能, 细胞学技术, 宫颈细胞学, 阴道镜检查, 筛查, 诊断, 鉴别, 治疗

Aiyuan WU,·Rezhake REMILA,Youlin QIAO. Progress and Challenge of Artificial Intelligence in Diagnosis and Treatment of Cervical Lesions[J]. Chinese General Practice, 2022, 25(18): 2215-2222. DOI: 10.12114/j.issn.1007-9572.2022.0095.
武爱媛,热米拉·热扎克,乔友林. 人工智能在宫颈病变诊断及治疗中的应用进展与挑战[J]. 中国全科医学, 2022, 25(18): 2215-2222. DOI: 10.12114/j.issn.1007-9572.2022.0095.

Figures/Tables 3

References 49

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作者	时间（年）	地点	例数	图像数据集	种类	细胞	训练软件	优势
BAO等^[13]	2020	多中心观察研究	2 145	8 329张数字细胞图像	TBS制片	188 542张图块（121 940张异常/66 602张正常	ResNet	初筛/分诊中在诊断CIN 2+级的准确性较优
BAO等^[14]	2020	多中心观察研究	703 103	8 329份细胞学样本	TBS制片	103 793份图像（32 859份异常/70 934份正常	深度学习算法	识别大部分正常细胞
TANG等^[15]	2021	深圳市妇幼保健院	10 601	2 167份细胞学样本	TBS制片	42 078异常细胞	RetinaNet	实时辅助诊断
WENTZENSEN等^[16]	2020	俄克拉荷马大学健康科学中心	4 253	431份样本训练/3 504份样本验证	P16/Ki-67双重染色涂片	431份图像〔8 956 DS（+）/10 692 DS（-）图块训练〕	CNN	具有自动化、客观性及较强的稳定性
ZHU等^[17]	2021	中国5个医疗中心	未知	81 727份样本训练/34，403份样本验证	TBS制片	未知	AIATBS	适用于不同设备，阅片速度<180 s/玻片
LI等^[18]	2021	宫颈细胞学图像数据集	未知	500张阳性/300张阴性	巴氏涂片	未知	Faster R-CNN-FPN模型	自动识别检测整张巴氏图像

作者	时间（年）	地点	例数	图像数据集	种类	细胞	训练软件	优势
BAO等^[13]	2020	多中心观察研究	2 145	8 329张数字细胞图像	TBS制片	188 542张图块（121 940张异常/66 602张正常	ResNet	初筛/分诊中在诊断CIN 2+级的准确性较优
BAO等^[14]	2020	多中心观察研究	703 103	8 329份细胞学样本	TBS制片	103 793份图像（32 859份异常/70 934份正常	深度学习算法	识别大部分正常细胞
TANG等^[15]	2021	深圳市妇幼保健院	10 601	2 167份细胞学样本	TBS制片	42 078异常细胞	RetinaNet	实时辅助诊断
WENTZENSEN等^[16]	2020	俄克拉荷马大学健康科学中心	4 253	431份样本训练/3 504份样本验证	P16/Ki-67双重染色涂片	431份图像〔8 956 DS（+）/10 692 DS（-）图块训练〕	CNN	具有自动化、客观性及较强的稳定性
ZHU等^[17]	2021	中国5个医疗中心	未知	81 727份样本训练/34，403份样本验证	TBS制片	未知	AIATBS	适用于不同设备，阅片速度<180 s/玻片
LI等^[18]	2021	宫颈细胞学图像数据集	未知	500张阳性/300张阴性	巴氏涂片	未知	Faster R-CNN-FPN模型	自动识别检测整张巴氏图像

作者	时间（年）	地点	例数	图像数据集	种类	正常	LSIL	HSIL	肿瘤	训练软件	优势	备注
SATO等^[29]	2018	日本埼玉县癌症中心	158	485	阴道镜图像	0	142^a		343	Keras神经网络/Tensor Flow库	阴道镜图像病变级别分类	单纯验证
HU等^[30]	2019	美国哥斯达黎加省	9 406	9 406	阴道镜图像	279^b		9 127^c		Faster R-CNN	支持数字阴道镜图像的自动视觉评估	训练集：744张图像，测试集：324张图像，筛查集：8 259张图像^d
MIYAGI等^[31]	2019	国立医院组织妇产科	330	未知	阴道镜图像	0	97	213	30	CNN	HSIL/LSIL分类	—
MIYAGI等^[32]	2020	柴达木医科大学国际医学中心	253	未知	阴道镜图像	0	210	43	0	CNN+HPV分型	AI与阴道镜及HPV分型结合用于HSIL/LSIL分类	—
CHO等^[33]	2020	韩国多中心大学附属医院	791	791	阴道镜图像	126	100	522	43	Inception-Resnet-v2模型/Resnet-152模型，Keras检测病变区域	检测HSIL+及宫颈病变区域预测分析	—
XUE等^[35]	2020	中国6家医院	19 435	101 267	阴道镜图像	6 013	5 932	6 389	1 101	CAIADS训练+优化+验证为7∶1∶2	HSIL+/LSIL+分级及宫颈病变区域预测分析	训练集：13 604例患者86 037张图像；优化：1 944例患者9 751张图像；验证：3 887例患者23 479张图像
LIU等^[36]	2021	山东齐鲁医院	7 530	15 276	阴道镜图像	3 966	1 411	1 966	157	ResNet（训练+验证+测试：7∶1∶2）	AI与宫颈疾病临床特征结合用于宫颈病变分类	训练集：5 271例患者10 650张图像，验证集：753名例患者1 531张，测试集：1 506例患者3 095张图像

作者	时间（年）	地点	例数	图像数据集	种类	正常	LSIL	HSIL	肿瘤	训练软件	优势	备注
SATO等^[29]	2018	日本埼玉县癌症中心	158	485	阴道镜图像	0	142^a		343	Keras神经网络/Tensor Flow库	阴道镜图像病变级别分类	单纯验证
HU等^[30]	2019	美国哥斯达黎加省	9 406	9 406	阴道镜图像	279^b		9 127^c		Faster R-CNN	支持数字阴道镜图像的自动视觉评估	训练集：744张图像，测试集：324张图像，筛查集：8 259张图像^d
MIYAGI等^[31]	2019	国立医院组织妇产科	330	未知	阴道镜图像	0	97	213	30	CNN	HSIL/LSIL分类	—
MIYAGI等^[32]	2020	柴达木医科大学国际医学中心	253	未知	阴道镜图像	0	210	43	0	CNN+HPV分型	AI与阴道镜及HPV分型结合用于HSIL/LSIL分类	—
CHO等^[33]	2020	韩国多中心大学附属医院	791	791	阴道镜图像	126	100	522	43	Inception-Resnet-v2模型/Resnet-152模型，Keras检测病变区域	检测HSIL+及宫颈病变区域预测分析	—
XUE等^[35]	2020	中国6家医院	19 435	101 267	阴道镜图像	6 013	5 932	6 389	1 101	CAIADS训练+优化+验证为7∶1∶2	HSIL+/LSIL+分级及宫颈病变区域预测分析	训练集：13 604例患者86 037张图像；优化：1 944例患者9 751张图像；验证：3 887例患者23 479张图像
LIU等^[36]	2021	山东齐鲁医院	7 530	15 276	阴道镜图像	3 966	1 411	1 966	157	ResNet（训练+验证+测试：7∶1∶2）	AI与宫颈疾病临床特征结合用于宫颈病变分类	训练集：5 271例患者10 650张图像，验证集：753名例患者1 531张，测试集：1 506例患者3 095张图像

数量	作者	时间（年）	数据集	使用数据	AI模型	应用
3项^a	URUSHIBARA	2020	418	MRI	CNN	诊断宫颈癌分期
	WANG	2020	95	MRI	SVM	诊断宫颈癌分期
	SOUMYA	2020	24	MRI	SVM	诊断宫颈癌分期
15项^b	CHEN	2020	127	CT	SVM	淋巴结转移
	TIAN	2020	277	CT	RF	新辅助化疗的应答
	YAN	2020	190	MRI	CNN	淋巴结转移
	DONG	2020	226	MRI	CNN	淋巴结转移
	WU	2020	894	MRI	CNN	淋巴结转移
	WANG	2020	137	MRI	SVM	子宫旁组织的浸润
	HUA	2020	111	MRI	CNN	子宫旁组织的浸润
	WANG	2019	96	MRI	SVM	淋巴结转移
	WU	2019	189	MRI	SVM/DT	淋巴结转移
	TAKADA	2020	87	MRI	RF	同步放化疗后复发
	PARK	2020	93	MRI	RF	同步放化疗后复发（中位随访38个月）
	MENG J	2018	34	MRI	SVM	同步放化疗后复发（中位随访31个月）
	TORHEIM T	2014	81	MRI	SVM	同步放化疗后复发（中位随访56个月）
	SHEN	2019	142	PET-CT	CNN	同步放化疗后复发（中位随访40个月）
	ROMAN JG	2016	53	PET-CT	RF	同步放化疗后复发（中位随访42个月）
10项^c	WEEGAR	2020	175 533	临床指标	SVM/NB/RF	诊断
	ASADI	2020	145	临床指标	SVM/ANN	诊断
	IJAZ	2020	858	临床指标	SVM/RF/KNN/ANN/NB	诊断
	ALSMARITY	2020	858	临床指标	DT/RF	诊断
	NITHYA	2019	858	临床指标	SVM/RF/KNN/RPART/C5.0	诊断
	SUMAN	2019	858	临床指标	ANN/SVM/RF/DT/Ada/Bayes	诊断
	GEETHA	2019	858	临床指标	RF	诊断
	FERNANDES	2018	858	临床指标	ANN/SVM/KNN/DT	诊断
	ABDOH	2018	858	临床指标	RF	诊断
	WU	2017	858	临床指标	SVM	诊断
6项^d	XIE	2020	647	临床指标	SVM	无进展生存期和总生存期(时间长短)
	MATSUO	2019	768	临床指标	ANN	无进展生存期和总生存期(时间长短)
	MATSUO	2017	157	临床指标	ANN	复发后3个月和6个月生存率
	OBRZUT	2017	102	临床指标	ANN/SVM/GEP/Κ-Means method	5年总生存率
	PAPADIA	2015	101	临床指标	ANN	新辅助化疗和根治性手术后需要辅助放疗
	TSENG	2013	168	临床指标	SVM/C5.0/ELM	复发(随访时间不明确)

Progress and Challenge of Artificial Intelligence in Diagnosis and Treatment of Cervical Lesions

人工智能在宫颈病变诊断及治疗中的应用进展与挑战

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