ArticleOncology letters2020
Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types.
Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 56 citations in OpenAlex.
- [An auxiliary diagnosis system for cervical intraepithelial neoplasia based on colposcopic images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Characteristics of fetal facial expression changes using artificial intelligence: a pilot study.Scientific reports · 2025Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Review
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- Deep dive into deep learning methods for cervical cancer detection and classification.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Review
- Advances in artificial intelligence for the early detection of cervical cancer in adult women: a scoping review.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2025Article
- A Systematic Review of the Application of Artificial Intelligence in Colposcopy: Diagnostic Accuracy for Cervical Intraepithelial Neoplasia and Cervical Cancer.Clinical Medicine Insights. Oncology · 2025Review
- A bibliometric analysis of artificial intelligence applied to cervical cancer.Frontiers in medicine · 2025Article
- Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders.Scientific reports · 2024Article
- Characterization of human papillomavirus genotypes and their coverage in vaccine delivered to Ethiopian women.Scientific reports · 2024Article
- Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.Journal of clinical medicine · 2024Review
- Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia.Scientific reports · 2024Article
- Detecting and localizing cervical lesions in colposcopic images with deep semantic feature mining.Frontiers in oncology · 2024Article
- MFEM-CIN: A Lightweight Architecture Combining CNN and Transformer for the Classification of Pre-Cancerous Lesions of the Cervix.IEEE open journal of engineering in medicine and biology · 2024Article
- Artificial intelligence and visual inspection in cervical cancer screening.International journal of gynecological cancer : official journal of the International Gynecological Cancer Society · 2023Article
- Artificial intelligence in colposcopic examination: A promising tool to assist junior colposcopists.Frontiers in medicine · 2023Article
- Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images.Diagnostics (Basel, Switzerland) · 2022Article
- Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis.Journal of gynecologic oncology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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