Page 49 - 中国全科医学2022-18
P. 49
·2222· http://www.chinagp.net E-mail:zgqkyx@chinagp.net.cn
facilitated by an artificial intelligence microscope:a preliminary [28]赵宇倩 . 适合中国不同资源地区的宫颈癌筛查技术及阴道镜检
study[J]. Cancer Cytopathol,2021,129(9):693-700. 查中组织学活检的探讨[D]. 北京:北京协和医学院,2016.
DOI:10.1002/cncy.22425. [29]王娜,王悦 . 人工智能在子宫颈癌筛查中的应用[J]. 中华妇
[16]WENTZENSEN N,LAHRMANN B,CLARKE M A,et al. Accuracy 产科杂志,2020,55(11):4. DOI:10.3760/cma.j.cn112141-
and efficiency of deep-learning-based automation of dual stain 20200405-00299.
cytology in cervical cancer screening[J]. J Natl Cancer Inst, [30]SATO M,HORIE K,HARA A,et al. Application of deep learning
2021,113(1):72-79. DOI:10.1093/jnci/djaa066. to the classification of images from colposcopy[J]. Oncol Lett,
[17]ZHU X H,LI X M,ONG K,et al. Hybrid AI-assistive diagnostic 2018:15(3):3518-3523. DOI:10.3892/ol.2018.7762.
model permits rapid TBS classification of cervical liquid-based thin- [31]HU L,BELL D,ANTANI S,et al. An observational study of deep
layer cell smears[J]. Nat Commun,2021,12(1):3541. learning and automated evaluation of cervical images for cancer
DOI:10.1038/s41467-021-23913-3. screening[J]. J Natl Cancer Inst,2019,111(9):923-932.
[18]LI X,XU Z H,SHEN X,et al. Detection of cervical cancer cells DOI:10.1093/jnci/djy225.
in whole slide images using deformable and global context aware [32]MIYAGI Y,TAKEHARA K,MIYAKE T. Application of deep
faster RCNN-FPN[J]. Curr Oncol,2021,28(5):3585- learning to the classification of uterine cervical squamous epithelial
3601. DOI:10.3390/curroncol28050307. lesion from colposcopy images[J]. Mol Clin Oncol,2019,11(6):
[19]SENKOMAGO V,ROYALTY J,MILLER J W,et al. Cervical 583-589. DOI:10.3892/mco.2019.1932.
[33]MIYAGI Y,TAKEHARA K,NAGAYASU Y,et al. Application
cancer screening in the National Breast and Cervical Cancer Early
of deep learning to the classification of uterine cervical squamous
Detection Program (NBCCEDP) in four US-Affiliated Pacific
epithelial lesion from colposcopy images combined with HPV
Islands between 2007 and 2015[J]. Cancer Epidemiol,2017,
types[J]. Oncol Lett,2020,19(2):1602-1610. DOI:
50:260-267. DOI:10.1016/j.canep.2017.04.011.
10.3892/ol.2019.11214.
[20]DEVI M A,RAVI S,VAISHNAVI J,et al. Classification
[34]CHO B J,CHOI Y J,LEE M J,et al. Classification of cervical
of cervical cancer using artificial neural networks[J].
neoplasms on colposcopic photography using deep learning[J].
Procedia Comput Sci,2016,89:465-472. DOI:10.1016/j.
Sci Rep,2020,10(1):13652. DOI:10.1038/s41598-020-
procs.2016.06.105.
70490-4.
[21]THRALL M J. Automated screening of Papanicolaou tests:a review
[35]薛鹏,乔友林,江宇 . 人工智能在医学内窥镜诊断中的应用[J].
of the literature[J]. Diagn Cytopathol,2019,47(1):20-27.
中华肿瘤杂志,2018,40(12):890-893. DOI:10.3760/cma.
DOI:10.1002/dc.23931.
j.issn.0253-3766.2018.12.003.
[22]RENSHAW A A,HOLLADAY E B,GEILS K B. Results of
XUE P,QIAO Y L,JIANG Y. Application of artificial intelligence
multiple-slide,blinded review of Papanicolaou slides in the
in diagnosis of medical endoscope[J]. Chinese Journal of
context of litigation. Determining what can be detected regularly
Oncology,2018,40(12):890-893. DOI:10.3760/cma.
and reliably[J]. Cancer,2005,105(5):263-269. DOI:
j.issn.0253-3766.2018.12.003.
10.1002/cncr.21319.
[36]XUE P,TANG C,LI Q,et al. Development and validation of an
[23]DOORNEWAARD H,VAN DEN TWEEL J G,JONES H W,
artificial intelligence system for grading colposcopic impressions and
3rd. Applications of automation in cervical cancer screening[J].
guiding biopsies[J]. BMC Med,2020,18(1):406. DOI:
J Low Genit Tract Dis,1998,2(1):19-24. DOI:
10.1186/s12916-020-01860-y.
10.1097/00128360-199801000-00005.
[37]LIU L,WANG Y,LIU X L,et al. Computer-aided diagnostic
[24]中国病理医师协会数字病理与人工智能病理学组,中华医学会
system based on deep learning for classifying colposcopy
病理学分会数字病理与人工智能工作委员会,中华医学会病理
images[J]. Ann Transl Med,2021,9(13):1045. DOI:
学分会细胞病理学组 . 宫颈液基细胞学的数字病理图像采集与
10.21037/atm-21-885.
图像质量控制中国专家共识[J]. 中华病理学杂志,2021,50
[38]薛鹏,唐朝,乔友林,等 . 人工智能电子阴道镜辅助诊断系统
(4):4. DOI:10.3760/cma.j.cn112151-20210111-00028.
对宫颈癌筛查的现实挑战和未来机遇[J]. 中国肿瘤,2019,
[25]OGILVIE G,NAKISIGE C,HUH W K,et al. Optimizing
28(7):483-486. DOI:10.11735/j.issn.1004-0242.2019.07.
secondary prevention of cervical cancer:recent advances and future
A001.
challenges[J]. Int J Gynaecol Obstet,2017,138(Suppl 1):
XUE P,TANG C,QIAO Y L,et al. Artificial intelligence
15-19. DOI:10.1002/ijgo.12187.
electronic colposcopy assisted diagnosis system for cervical cancer
[26]XUE P,NG M T A,QIAO Y L. The challenges of colposcopy
screening:challenge and prospective[J]. China Cancer,2019,
for cervical cancer screening in LMICs and solutions by artificial
28(7):483-486. DOI:10.11735/j.issn.1004-0242.2019.07.
intelligence[J]. BMC Med,2020,18:169. DOI: A001.
10.1186/s12916-020-01613-x. [39]SOUMYA K,SNEHA K,ARUNVINODH C. Cervical cancer
[27]赵昀,魏丽惠 . 我国阴道镜技术培训何去何从[J]. 中国妇产 detection and classification using texture analysis[J]. Biomed
科临床杂志,2019,20(1):1-2. DOI:10.13390/j.issn.1672- Pharmacol J,2016,9(2):663-671. DOI:10.13005/bpj/988.
1861.2019.01.001. (下转第2230页)