ArticleScientific reports2020
Classification of cervical neoplasms on colposcopic photography using deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed, 102 citations in OpenAlex.
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- Cervical Intraepithelial Neoplasia (CIN1-3) Disease Grading Using a Mixture of Experts Approach.Journal of imaging informatics in medicine · 2026Article
- Bringing Cervical Cancer Screening Closer to Women: Feasibility of Artificial Intelligence and Remote Assessment in Primary Health Care.International journal of public health · 2026Article
- Integrating Biomarkers into Cervical Cancer Screening-Advances in Diagnosis and Risk Prediction: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- 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
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology.BMC cancer · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- Eliminating VEGFA+ tumor-associated neutrophils by antibody-drug conjugates boosts antitumor immunity and potentiates PD-1 immunotherapy in preclinical models of cervical cancer.Cell death & disease · 2025Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Automated Image Clarity Detection for the Improvement of Colposcopy Imaging with Multiple Devices.Biomedical signal processing and control · 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
- Employing machine learning models to predict pregnancy termination among adolescent and young women aged 15-24 years in East Africa.Scientific reports · 2024Article
- Artificial intelligence strengthens cervical cancer screening - present and future.Cancer biology & medicine · 2024Review
- Usability of a smartphone-compatible, confocal micro-endoscope for cervical cancer screening in resource-limited settings.BMC women's health · 2024Article
- Clinical evaluation of an artificial intelligence-assisted cytological system among screening strategies for a cervical cancer high-risk population.BMC cancer · 2024Article
- Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks.BMC health services research · 2024Article
- Deep feature extraction and fine κ-nearest neighbour for enhanced human papillomavirus detection in cervical cancer - a comprehensive analysis of colposcopy images.Contemporary oncology (Poznan, Poland) · 2024Article
- Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.Postgraduate medical journal · 2023Review
- A segmentation model to detect cevical lesions based on machine learning of colposcopic images.Heliyon · 2023Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing countries. Artificial intelligence (AI) has been recently used in computer-aided diagnosis showing remarkable promise. In this study, we developed and validated deep learning models to automatically classify cervical neoplasms on colposcopic photographs. Pre-trained convolutional neural networks were fine-tuned for two grading systems: the cervical intraepithelial neoplasia (CIN) system and the lower anogenital squamous terminology (LAST) system. The multi-class classification accuracies of the networks for the CIN system in the test dataset were 48.6 ± 1.3% by Inception-Resnet-v2 and 51.7 ± 5.2% by Resnet-152. The accuracies for the LAST system were 71.8 ± 1.8% and 74.7 ± 1.8%, respectively. The area under the curve (AUC) for discriminating high-risk lesions from low-risk lesions by Resnet-152 was 0.781 ± 0.020 for the CIN system and 0.708 ± 0.024 for the LAST system. The lesions requiring biopsy were also detected efficiently (AUC, 0.947 ± 0.030 by Resnet-152), and presented meaningfully on attention maps. These results may indicate the potential of the application of AI for automated reading of colposcopic photographs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.