ArticleBMC research notes2022
Cervical pre-cancerous lesion detection: development of smartphone-based VIA application using artificial intelligence.
Article in BMC research notes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- Applying an AI Decision Support Device During Cervical Cancer Screening in Bangladesh and Uganda: Implementation Study.JMIR formative research · 2026Trial
- Review
- Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer.Oncology reviews · 2026Review
- Artificial intelligence and digital health equity: a post-pandemic evidence synthesis and implementation safeguards framework.Frontiers in digital health · 2026Review
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 2025Article
- Results comparison of cervical cancer early detection using cerviray ® with VIA test.BMC research notes · 2025Observational
- Deep dive into deep learning methods for cervical cancer detection and classification.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Review
- The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case.Journal of neurosurgery. Case lessons · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveVisual inspection of cervix after acetic acid application (VIA) has been considered an alternative to Pap smear in resource-limited settings, like Indonesia. However, VIA results mainly depend on examiner's experience and with the lack of comprehensive training of healthcare workers, VIA accuracy keeps declining. We aimed to develop an artificial intelligence (AI)-based Android application that can automatically determine VIA results in real time and may be further developed as a health care support system in cervical cancer screening.
resultA total of 199 women who underwent VIA test was studied. Images of cervix before and after VIA test were taken with smartphone, then evaluated and labelled by experienced oncologist as VIA positive or negative. Our AI model training pipeline consists of 3 steps: image pre-processing, feature extraction, and classifier development. Out of the 199 data, 134 were used as train-validation data and the remaining 65 data were used as test data. The trained AI model generated a sensitivity of 80%, specificity of 96.4%, accuracy of 93.8%, precision of 80%, and ROC/AUC of 0.85 (95% CI 0.66-1.0). The developed AI-based Android application may potentially aid cervical cancer screening, especially in low resource settings.
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.