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, 19 (2), 1602-1610

Application of Deep Learning to the Classification of Uterine Cervical Squamous Epithelial Lesion From Colposcopy Images Combined With HPV Types

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Application of Deep Learning to the Classification of Uterine Cervical Squamous Epithelial Lesion From Colposcopy Images Combined With HPV Types

Yasunari Miyagi et al. Oncol Lett.

Abstract

The aim of the present study was to explore the feasibility of using deep learning, such as artificial intelligence (AI), to classify cervical squamous epithelial lesions (SILs) from colposcopy images combined with human papilloma virus (HPV) types. Among 330 patients who underwent colposcopy and biopsy performed by gynecological oncologists, a total of 253 patients with confirmed HPV typing tests were enrolled in the present study. Of these patients, 210 were diagnosed with high-grade SIL (HSIL) and 43 were diagnosed with low-grade SIL (LSIL). An original AI classifier with a convolutional neural network catenating with an HPV tensor was developed and trained. The accuracy of the AI classifier and gynecological oncologists was 0.941 and 0.843, respectively. The AI classifier performed better compared with the oncologists, although not significantly. The sensitivity, specificity, positive predictive value, negative predictive value, Youden's J index and the area under the receiver-operating characteristic curve ± standard error for AI colposcopy combined with HPV types and pathological results were 0.956 (43/45), 0.833 (5/6), 0.977 (43/44), 0.714 (5/7), 0.789 and 0.963±0.026, respectively. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL by both colposcopy and HPV type may be feasible.

Keywords: HPV; artificial intelligence; cervical intraepithelial neoplasia; colposcopy; deep learning.

Figures

Figure 1.
Figure 1.
Development the AI classifier with deep learning for colposcopy images and HPV types. HPV, human papilloma virus; LSIL, low-grade squamous intraepithelial lesion; HSIL, high-grade squamous intraepithelial lesion; AI, artificial intelligence.

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References

    1. Müller VC, Bostrom N, editors. Springer; Cham: 2016. Future progress in artificial intelligence: A survey of expert opinion. In: Fundamental Issues of Artificial Intelligence; pp. 555–572.
    1. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, et al. Mastering the game of go without human knowledge. Nature. 2017;550:354–359. doi: 10.1038/nature24270. - DOI - PubMed
    1. Miyagi Y, Takehara K, Miyake T. Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images. Mol Clin Oncol. 2019;11:583–589. - PMC - PubMed
    1. Miyagi Y, Habara T, Hirata R, Hayashi N. Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age. Reprod Med Biol. 2019;18:344–356. doi: 10.1002/rmb2.12284. - DOI - PMC - PubMed
    1. Arbyn M, Castellsagué X, de Sanjosé S, Bruni L, Saraiya M, Bray F, Ferlay J. Worldwide burden of cervical cancer in 2008. Ann Oncol. 2011;22:2675–2686. doi: 10.1093/annonc/mdr015. - DOI - PubMed

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