Artificial Intelligence and Colposcopy: Automatic Identification of Vaginal Squamous Cell Carcinoma Precursors.

Simple Summary: A colposcopy provides a comprehensive examination of the female genital tract, including the cervix, vagina, and vulva. However, its diagnostic accuracy, especially for vaginal lesions, remains suboptimal due to the challenge in detecting subtle alterations. Integrating artificial intelligence (AI) into colposcopy holds potential to enhance the detection rates of clinically important lesions. Our study pioneered the development of an AI algorithm capable of differentiating low-grade (LSILs) and high-grade (HSILs) squamous intraepithelial lesions in the vagina. The promising results we achieve in differentiating HPV-associated dysplastic lesions demonstrate that AI can significantly address the current challenges in medical practice. There are already promising results when using AI to detect HPV lesions in the cervix and anus, suggesting that in the future, this ubiquitous tool for lesion detection across different anatomical regions could be a reality. The future integration of these technologies into the colposcopy process could revolutionize healthcare, making early detection a reality for women worldwide. Background/Objectives: While human papillomavirus (HPV) is well known for its role in cervical cancer, it also affects vaginal cancers. Although colposcopy offers a comprehensive examination of the female genital tract, its diagnostic accuracy remains suboptimal. Integrating artificial intelligence (AI) could enhance the cost-effectiveness of colposcopy, but no AI models specifically differentiate low-grade (LSILs) and high-grade (HSILs) squamous intraepithelial lesions in the vagina. This study aims to develop and validate an AI model for the differentiation of HPV-associated dysplastic lesions in this region. Methods: A convolutional neural network (CNN) model was developed to differentiate HSILs from LSILs in vaginoscopy (during colposcopy) still images. The AI model was developed on a dataset of 57,250 frames (90% training/validation [including a 5-fold cross-validation] and 10% testing) obtained from 71 procedures. The model was evaluated based on its sensitivity, specificity, accuracy and area under the receiver operating curve (AUROC). Results: For HSIL/LSIL differentiation in the vagina, during the training/validation phase, the CNN demonstrated a mean sensitivity, specificity and accuracy of 98.7% (IC95% 96.7–100.0%), 99.1% (IC95% 98.1–100.0%), and 98.9% (IC95% 97.9–99.8%), respectively. The mean AUROC was 0.990 ± 0.004. During testing phase, the sensitivity was 99.6% and 99.7% for both specificity and accuracy. Conclusions: This is the first globally developed AI model capable of HSIL/LSIL differentiation in the vaginal region, demonstrating high and robust performance metrics. Its effective application paves the way for AI-powered colposcopic assessment across the entire female genital tract, offering a significant advancement in women's healthcare worldwide. [ABSTRACT FROM AUTHOR]