# An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening

Source: https://onco.cc/key-papers/paper-hu-j-natl-cancer-inst/  
OnCo record `paper-hu-j-natl-cancer-inst` (Key paper). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Paper cited by one technology page, indexed on Europe PMC as PubMed record 30629194 and published in JNCI: Journal of the National Cancer Institute; the citing page links this DOI, which is how the record was matched.

## Summary

Background: Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid application is practical but not reproducible or accurate. The objective of this study was to develop a "deep learning"-based visual evaluation algorithm that automatically recognizes cervical precancer/cancer.

Methods: A population-based longitudinal cohort of 9406 women ages 18-94 years in Guanacaste, Costa Rica was followed for 7 years (1993-2000), incorporating multiple cervical screening methods and histopathologic confirmation of precancers. Tumor registry linkage identified cancers up to 18 years. Archived, digitized cervical images from screening, taken with a fixed-focus camera ("cervicography"), were used for training/validation of the deep learning-based algorithm. The resultant image prediction score (0-1) could be categorized to balance sensitivity and specificity for detection of precancer/cancer. All statistical tests were two-sided.

Results: Automated visual evaluation of enrollment cervigrams identified cumulative precancer/cancer cases with greater accuracy (area under the curve [AUC] = 0.91, 95% confidence interval [CI] = 0.89 to 0.93) than original cervigram interpretation (AUC = 0.69, 95% CI = 0.63 to 0.74; P <.001) or conventional cytology (AUC = 0.71, 95% CI = 0.65 to 0.77; P <.001). A single visual screening round restricted to women at the prime screening ages of 25-49 years could identify 127 (55.7%) of 228 precancers (cervical intraepithelial neoplasia 2/cervical intraepithelial neoplasia 3/adenocarcinoma in situ [AIS]) diagnosed cumulatively in the entire adult population (ages 18-94 years) while referring 11.0% for management.

Conclusions: The results support consideration of automated visual evaluation of cervical images from contemporary digital cameras. If achieved, this might permit dissemination of effective point-of-care cervical screening.

Indexed on Europe PMC as PubMed record 30629194 (DOI 10.1093/jnci/djy225). Matched by DOI alone: one technology page cites this DOI among its external links (the pages are listed under Related), and this page was written so that the citation resolves inside OnCo. No figure has been checked by an editor.

## Fields

- Kind: Key paper
- Last checked: 2026-09-22
- Tags: europepmc-ingest
- Journal: JNCI: Journal of the National Cancer Institute
- Year: 2019
- DOI: 10.1093/jnci/djy225
- Authors: Hu L, Bell D, Antani S, et al.
- What it means: One technology page on OnCo cites this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing page listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
- Caveats: Matched to the citing OnCo records by DOI alone; the summary reproduces the Europe PMC abstract and no figure has been verified against the full paper.

## Sources

- J Natl Cancer Inst 2019: https://doi.org/10.1093/jnci/djy225
- PubMed: https://pubmed.ncbi.nlm.nih.gov/30629194/
- Europe PMC: https://europepmc.org/article/MED/30629194

## Connected records

- technologies: [Colposcopes and digital cervical screening devices (DYSIS, EVA System, AVE)](https://onco.cc/technologies/colposcopes-digital-cervical-screening/)
- journals: [JNCI: Journal of the National Cancer Institute](https://onco.cc/journals/jnci/)

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