Higher Diagnostic Accuracy of an AI Model for Colposcopy Compared With Conventional and Digital Colposcopic Evaluation.

OBJECTIVE: Colposcopy involves subjective visual assessment of cervical features that may indicate cervical dysplasia. Pattern recognition during colposcopy could be enhanced by artificial intelligence (AI). Using colposcopy images with precisely mapped multiple biopsy sites and corresponding histologic diagnoses, we developed an AI model, Cervix-AID-Net, to classify colposcopy images into low-grade disease [less than cervical intraepithelial neoplasia (CIN) grade 2] and high-grade disease (CIN grade 2 or above). The objective of this study was to compare the diagnostic performance of the Cervix-AID-Net model with the digital colposcope (DySIS) color map and colposcopists' interpretations of the cervix in identifying low-grade and high-grade disease. METHODS: The authors used 3,153 colposcopy images from 178 women, each with 4 biopsies, to train and validate the algorithm. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated with 95% CIs. RESULTS: Cervix-AID-Net achieved a diagnostic accuracy of 99.8% (95% CI: 99.6-99.9) in classifying colposcopy images into low-grade and high-grade categories. This was significantly higher than the DySIS color map accuracy of 58.8% (95% CI: 51.1-66.1) and the accuracy of the colposcopist's visual impression of the cervix (55.1%, 95% CI: 47.2%-62.5%). CONCLUSION: This first version of the Cervix-AID-Net demonstrated superior diagnostic accuracy compared with both the DySIS color map and colposcopists' visual assessment. The results need confirmation in a prospective clinical trial.