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

ABSTRACT:

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

SUBMITTER: Hu L 

PROVIDER: S-EPMC6748814 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature