ArticleInternational journal of cancer2020
A demonstration of automated visual evaluation of cervical images taken with a smartphone camera.
Article in International journal of cancer, 2020. 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 47 papers.
What it found
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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
47 citing papers in PubMed, 71 citations in OpenAlex.
- From HPV infection to cervical precancer: Molecular mechanisms, immune microenvironment and emerging technologies.Cellular and molecular life sciences : CMLS · 2026Review
- Resource-Adapted Triage Strategies for Women Testing HPV Positive With Self-Collected Vaginal Samples in Cameroon.International journal of cancer · 2026Article
- Advancing the elimination of cervical cancer across Africa.Nature communications · 2026Review
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- Integrating Biomarkers into Cervical Cancer Screening-Advances in Diagnosis and Risk Prediction: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- AI-Driven Advances in Women's Health Diagnostics: Current Applications and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Advancing Cervical Cancer Care: A Comprehensive Study of Screening Approach for Tribal Women in Sub-Saharan Africa and Asia.Iranian journal of public health · 2025Review
- 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
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- Automated Image Clarity Detection for the Improvement of Colposcopy Imaging with Multiple Devices.Biomedical signal processing and control · 2025Article
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- Utility of colposcopy for the screening and management of cervical cancer in Africa: a cross-sectional analysis of providers' training and practices.BMC health services research · 2024Article
- Automated analysis of digital medical images in cervical cancer screening: A systematic review.medRxiv : the preprint server for health sciences · 2024Article
- Artificial intelligence strengthens cervical cancer screening - present and future.Cancer biology & medicine · 2024Review
- Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening.Journal of the National Cancer Institute · 2024Article
- Optical imaging for screening and early cancer diagnosis in low-resource settings.Nature reviews bioengineering · 2024Article
- Reproducible and clinically translatable deep neural networks for cervical screening.Scientific reports · 2023Article
- Benefits, harms and cost-effectiveness of cervical screening, triage and treatment strategies for women in the general population.Nature medicine · 2023Article
- A Review of Computer-Aided Diagnostic Algorithms for Cervical Neoplasia and an Assessment of Their Applicability to Female Genital Schistosomiasis.Mayo Clinic proceedings. Digital health · 2023Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
We examined whether automated visual evaluation (AVE), a deep learning computer application for cervical cancer screening, can be used on cervix images taken by a contemporary smartphone camera. A large number of cervix images acquired by the commercial MobileODT EVA system were filtered for acceptable visual quality and then 7587 filtered images from 3221 women were annotated by a group of gynecologic oncologists (so the gold standard is an expert impression, not histopathology). We tested and analyzed on multiple random splits of the images using two deep learning, object detection networks. For all the receiver operating characteristics curves, the area under the curve values for the discrimination of the most likely precancer cases from least likely cases (most likely controls) were above 0.90. These results showed that AVE can classify cervix images with confidence scores that are strongly associated with expert evaluations of severity for the same images. The results on a small subset of images that have histopathologic diagnoses further supported the capability of AVE for predicting cervical precancer. We examined the associations of AVE severity score with gynecologic oncologist impression at all regions where we had a sufficient number of cases and controls, and the influence of a woman's age. The method was found generally resilient to regional variation in the appearance of the cervix. This work suggests that using AVE on smartphones could be a useful adjunct to health-worker visual assessment with acetic acid, a cervical cancer screening method commonly used in low- and middle-resource settings.
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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.