Deep learning-assisted digital VIA via telemedicine and HPV self-sampling for CIN2+ detection in remote archipelago women: a prospective diagnostic accuracy study

Abstract

Background: Cervical cancer disproportionately burdens women in low- and middle-income countries, where archipelagic geography and colposcopist shortages obstruct screening. Human papillomavirus (HPV) self-sampling is highly sensitive but poorly specific, generating colposcopy referrals that exceed remote-area capacity.

Objective: To evaluate whether deep-learning (DL)-assisted digital visual inspection with acetic acid (VIA), delivered by telemedicine, can triage HPV-positive women and detect high-grade cervical intraepithelial neoplasia (CIN2+).

Methods: In a prospective, double-blind, STARD-compliant diagnostic accuracy study, 642 women aged 30–50 years at an urban tertiary referral hospital (Center A) and five remote community health centers (Region B) in an Indonesian archipelago province underwent HPV-DNA self-sampling and smartphone-captured digital VIA analyzed by a MobileNetV2 convolutional neural network. All participants received colposcopy-directed biopsy as the reference standard, eliminating verification bias. Metrics used Wilson 95% confidence intervals (CI); multivariable logistic regression and decision-analytic triage metrics were derived.

Results: CIN2+ prevalence was 13.1% (95% CI 10.7–15.9). DL-assisted VIA achieved sensitivity 91.7% (95% CI 83.8–95.9), specificity 88.4% (85.4–90.8), and AUC 0.93, exceeding human-read VIA (sensitivity 67.9%). HPV self-sampling was most sensitive (95.2%) but least specific (81.5%). A sequential HPV→DL-VIA pathway raised specificity to 95.7% and positive predictive value to 75.5%, reducing colposcopy referrals by 46.4% and unnecessary referrals by 76.7% (number-needed-to-screen 7.6). HPV positivity dominated the multivariable model (adjusted OR 91.5, 95% CI 32.7–256.2, p<0.001; Nagelkerke R² 0.50).

Conclusion: Telemedicine-delivered, DL-assisted VIA is an accurate triage for HPV-positive women that conserves scarce colposcopy capacity while preserving CIN2+ detection. This decentralized two-step pathway is a scalable strategy for advancing cervical-cancer elimination in geographically isolated populations.