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

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.

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

“…Recently, artificial intelligence (AI) methods have shown potential in subjective imaging diagnoses for malignancies such as breast cancer, colorectal cancer, and gastrointestinal cancer [11][12][13]. The application of similar methods to colposcopic imaging is not yet widespread [14,15]. In this study, we developed an AI method (Colposcopic Artificial Intelligence Auxiliary Diagnostic System [CAIADS]) for grading colposcopic impressions and guiding biopsies.…”

An artificial intelligence‑assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions

“…Furthermore, Xue et al (10) report that automated visual evaluation by smartphones can be a useful adjunct to health-worker visual assessment with acetic acid, a cervical cancer screening method commonly used in low-and middle-resource settings. Miyagi et al (11) report the feasibility of using deep learning to classify cervical squamous epithelial lesions (SILs) from colposcopy images combined with human papillomavirus (HPV) types. The sensitivity, specificity, positive predictive value, negative predictive value and the AUC ± 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) and 0.963±0.026, respectively.…”

“…This could be attributed to the evaluation of AI image diagnosis in four categories in the current study. In previous reports (7,(9)(10)(11)(12), the cervical pathology was divided into two or three categories. In the present study, the diagnostic accuracy when divided into two categories was 79.4% in HSIL and 87.0% in LSIL, comparable to other reports (7,(9)(10)(11)(12).…”

Application of artificial intelligence in gynecologic malignancies: A review

“…In addition to AI applications using colposcopy images alone, deep learning approaches also included HPV testing data. Miyagi et al 18 combined 253 colposcopy images containing CIN with HPV‐typing information and developed a CIN lesion diagnosis algorithm using deep learning. The diagnostic accuracy of gynecologic oncologists was 0.843, whereas the accuracy of the deep learning algorithm was 0.941 18 …”