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

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 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.