Research graph
References from Development and temporal validation of a proof-of-concept artificial intelligence model for binary classification of cervical cotest. Local targets link to admitted publications; unresolved targets remain external evidence.
Hysterectomy-corrected cervical cancer mortality rates reveal a larger racial disparity in the United States.
10.1002/cncr.30507 · 2017 · External reference
Artificial intelligence estimates the impact of human papillomavirus types in influencing the risk of cervical dysplasia recurrence: progress toward a more personalized approach.
10.1097/cej.0000000000000432 · 2019 · External reference
Cervical cancer screening in developing countries at a crossroad: emerging technologies and policy choices.
10.5306/wjco.v6.i6.281 · 2015 · External reference
Eurogin roadmap 2017: triage strategies for the management of HPV-positive women in cervical screening programs.
10.1002/ijc.31261 · 2018 · External reference
Overview of the European and North American studies on HPV testing in primary cervical cancer screening.
10.1002/ijc.21955 · 2006 · External reference
Papanicolaou test interpretation utilizing the Hologic Genius Digital Diagnostics System vs manual glass slide review: a retrospective study of 596 cases.
10.1093/ajcp/aqaf080 · 2025 · External reference
Gynecologic cytology proficiency testing failures: what have we learned? Observations from the College of American Pathologists gynecologic cytology proficiency testing program.
10.5858/arpa.2010-0681-sa · 2011 · External reference
Estimation of cancer deaths averted from prevention, screening, and treatment efforts, 1975–2020.
10.1001/jamaoncol.2024.5381 · 2025 · External reference
Point-of-care digital cytology with artificial intelligence for cervical cancer screening in a resource-limited setting.
10.1001/jamanetworkopen.2021.1740 · 2021 · External reference
Cervical cytology reproducibility and associated clinical and demographic factors.
10.1002/dc.24325 · 2020 · External reference
A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis.
10.1007/s10462-023-10588-z · 2023 · External reference
Screening for cervical cancer in primary care: a decision analysis for the US Preventive Services Task Force.
10.1001/jama.2017.19872 · 2018 · External reference
Detection of cervical cancer cells in whole slide images using deformable and global context aware faster RCNN-FPN.
10.3390/curroncol28050307 · 2021 · External reference
Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.
10.1016/j.eclinm.2024.102992 · 2024 · External reference
Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning-based techniques.
10.1002/dc.25295 · 2024 · External reference
Accuracy of the Papanicolaou test in screening for and follow-up of cervical cytologic abnormalities: a systematic review.
10.7326/0003-4819-132-10-200005160-00009 · 2000 · External reference
Measuring sensitivity in gynecologic cytology: a review.
10.1002/cncr.10717 · 2002 · External reference
Rethinking algorithm performance metrics for artificial intelligence in diagnostic medicine.
10.1001/jama.2022.10561 · 2022 · External reference
Changes in cervical cancer incidence following the introduction of organized screening in Italy.
10.1016/j.ypmed.2015.01.034 · 2015 · External reference
Interobserver reproducibility of cervical cytologic and histologic interpretations: realistic estimates from the ASCUS-LSIL Triage Study.
10.1001/jama.285.11.1500 · 2001 · External reference
A systematic review on deep learning-based automated cancer diagnosis models.
10.1111/jcmm.18144 · 2024 · External reference
Screening for cervical cancer: US Preventive Services Task Force recommendation statement.
10.1001/jama.2018.10897 · 2018 · External reference
Preventable fractions of cervical cancer via effective screening in six Baltic, central, and eastern European countries 2017–40: a population-based study.
10.1016/s1470-2045(16)30275-3 · 2016 · External reference
Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm.
10.1016/j.imu.2019.02.001 · 2019 · External reference
Interlaboratory variation in the performance of liquid-based cytology: insights from the ATHENA trial.
10.1002/ijc.28514 · 2014 · External reference
Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.
10.1038/s41467-025-58883-3 · 2025 · External reference
DeepPap: deep convolutional networks for cervical cell classification.
10.1109/jbhi.2017.2705583 · 2017 · External reference
