ArticleInternational journal of cancer2022
The development of "automated visual evaluation" for cervical cancer screening: The promise and challenges in adapting deep-learning for clinical testing: Interdisciplinary principles of automated visual evaluation in cervical screening.
Article in International journal of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06815939 (Validation of a Lab-free Low-cost Screening Test for Prevention of Cervical Cancer), which is not on this map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Validation of a Lab-free Low-cost Screening Test for Prevention of Cervical Cancer: Automated Visual Evaluation
Who cites it
38 citing papers in PubMed.
- Article
- RE: An observational study of deep learning and automated evaluation of cervical images for cancer screening.Journal of the National Cancer Institute · 2026Article
- The Callascope: A portable, user-friendly, speculum-free device for diagnostic imaging of cervical cancer precursor lesions.npj women's health · 2026Article
- A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques.Frontiers in artificial intelligence · 2026Review
- 'Female Genital Schistosomiasis: Translational Challenges and Opportunities' - outputs and actions from a consultative, collaborative and translational workshop.Parasitology · 2025Review
- Visual diagnostics for female genital schistosomiasis and the opportunity for improvement using computer vision.Parasitology · 2025Review
- Initial evaluation of a new cervical screening strategy combining human papillomavirus genotyping and automated visual evaluation: the Human Papillomavirus-Automated Visual Evaluation Consortium.Journal of the National Cancer Institute · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Relation to Accurate Information and Tasks in Gynecologic Oncology and Clinical Medicine-Dunning-Kruger Effects and Ultracrepidarianism.Diagnostics (Basel, Switzerland) · 2025Review
- Performance of visual inspection, partial genotyping, and their combination for the triage of women living with HIV who are screen positive for human papillomavirus: Results from the AIMA-CC ANRS 12375 multicentric screening study.International journal of cancer · 2025Article
- 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 cervical cancer control strategy for lower-resource settings: interventions to complement one-dose HPV vaccination.Journal of the National Cancer Institute. Monographs · 2024Article
- Automated analysis of digital medical images in cervical cancer screening: A systematic review.medRxiv : the preprint server for health sciences · 2024Article
- Epidemiology of HPV-associated cancers past, present and future: towards prevention and elimination.Nature reviews. Clinical oncology · 2024Review
- HPVsim: An agent-based model of HPV transmission and cervical disease.PLoS computational biology · 2024Article
- Internal validation of Automated Visual Evaluation (AVE) on smartphone images for cervical cancer screening in a prospective study in Zambia.Cancer medicine · 2024Article
- Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning.Scientific reports · 2024Article
- Squamocolumnar junction visibility, age, and implications for cervical cancer screening programs.Preventive medicine · 2024Article
- Internal Validation of Automated Visual Evaluation (AVE) on Smartphone Images for Cervical Cancer Screening in a Prospective Study in Zambia.medRxiv : the preprint server for health sciences · 2024Article
- Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy.eLife · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
There is limited access to effective cervical cancer screening programs in many resource-limited settings, resulting in continued high cervical cancer burden. Human papillomavirus (HPV) testing is increasingly recognized to be the preferable primary screening approach if affordable due to superior long-term reassurance when negative and adaptability to self-sampling. Visual inspection with acetic acid (VIA) is an inexpensive but subjective and inaccurate method widely used in resource-limited settings, either for primary screening or for triage of HPV-positive individuals. A deep learning (DL)-based automated visual evaluation (AVE) of cervical images has been developed to help improve the accuracy and reproducibility of VIA as assistive technology. However, like any new clinical technology, rigorous evaluation and proof of clinical effectiveness are required before AVE is implemented widely. In the current article, we outline essential clinical and technical considerations involved in building a validated DL-based AVE tool for broad use as a clinical test.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.