Open Access

Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types

  • Authors:
    • Yasunari Miyagi
    • Kazuhiro Takehara
    • Yoko Nagayasu
    • Takahito Miyake
  • View Affiliations

  • Published online on: December 12, 2019     https://doi.org/10.3892/ol.2019.11214
  • Pages: 1602-1610
  • Copyright: © Miyagi et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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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.
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February-2020
Volume 19 Issue 2

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Spandidos Publications style
Miyagi Y, Takehara K, Nagayasu Y and Miyake T: Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types. Oncol Lett 19: 1602-1610, 2020
APA
Miyagi, Y., Takehara, K., Nagayasu, Y., & Miyake, T. (2020). Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types. Oncology Letters, 19, 1602-1610. https://doi.org/10.3892/ol.2019.11214
MLA
Miyagi, Y., Takehara, K., Nagayasu, Y., Miyake, T."Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types". Oncology Letters 19.2 (2020): 1602-1610.
Chicago
Miyagi, Y., Takehara, K., Nagayasu, Y., Miyake, T."Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types". Oncology Letters 19, no. 2 (2020): 1602-1610. https://doi.org/10.3892/ol.2019.11214