ArticleMolecular and clinical oncology2019
Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images.
Article in Molecular and clinical oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 56 citations in OpenAlex.
- Modelling the epidemic dynamics of HPV among women in China and optimization of ongoing cervical cancer elimination strategies.BMC medicine · 2026Article
- AI-assisted diagnosis of cervical dysplasia from cervicography images.Scientific reports · 2026Article
- Automated detection and classification of cervical and anal squamous cancer precursors using deep learning and multidevice colposcopy.Scientific reports · 2025Article
- Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Review
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Review
- HMCFormer (hierarchical multi-scale convolutional transformer): a hybrid CNN+Transformer network for intelligent VIA screening.PeerJ. Computer science · 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
- CERVIXNET: An Efficient Approach for the Detection and Classifications of the Cervigram Images Using Modified Deep Learning Architecture.Current medical imaging · 2025Article
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- Development and Clinical Validation of Visual Inspection With Acetic Acid Application-Artificial Intelligence Tool Using Cervical Images in Screen-and-Treat Visual Screening for Cervical Cancer in South India: A Pilot Study.JCO global oncology · 2024Article
- A Dual-Branch Residual Network with Attention Mechanisms for Enhanced Classification of Vaginal Lesions in Colposcopic Images.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Colposcopy: Automatic Identification of Vaginal Squamous Cell Carcinoma Precursors.Cancers · 2024Article
- Artificial intelligence strengthens cervical cancer screening - present and future.Cancer biology & medicine · 2024Review
- Artificial Intelligence and Colposcopy: Automatic Identification of Cervical Squamous Cell Carcinoma Precursors.Journal of clinical medicine · 2024Article
- Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.Journal of clinical medicine · 2024Review
- Detecting and localizing cervical lesions in colposcopic images with deep semantic feature mining.Frontiers in oncology · 2024Article
- A segmentation model to detect cevical lesions based on machine learning of colposcopic images.Heliyon · 2023Article
- Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.Cancer medicine · 2023Article
- HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides.BioMed research international · 2023Article
Corrections and comments
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Authors and funding
3 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 as artificial intelligence (AI) to classify cervical squamous epithelial lesions (SIL) from colposcopy images. A total of 330 patients who underwent colposcopy and biopsy by gynecologic oncologists were enrolled in the current study. A total of 97 patients received a pathological diagnosis of low-grade SIL (LSIL) and 213 of high-grade SIL (HSIL). An original AI-classifier with 11 layers of the convolutional neural network was developed and trained. The accuracy, sensitivity, specificity and Youden's J index of the AI-classifier and oncologists for diagnosing HSIL were 0.823 and 0.797, 0.800 and 0.831, 0.882 and 0.773, and 0.682 and 0.604, respectively. The area under the receiver-operating characteristic curve was 0.826±0.052 (mean ± standard error), and the 95% confidence interval 0.721-0.928. The optimal cut-off point was 0.692. Cohen's Kappa coefficient for AI and colposcopy was 0.437 (P<0.0005). The AI-classifier performed better than oncologists, although not significantly. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL from by colposcopy may be feasible.
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