# Artificial Intelligence and Colposcopy: Detection and Classification of Vulvar HPV-Related Low-Grade and High-Grade Squamous Intraepithelial Lesions

## TL;DR

This paper introduces the first AI model to detect and classify HPV-related vulvar lesions using vulvoscopy images, achieving high accuracy.

## Contribution

The study presents the first convolutional neural network for automated detection and classification of vulvar HSIL and LSIL.

## Key findings

- The CNN model achieved 99.7% recall and 99.1% precision in lesion detection and classification.
- The model was trained and validated using 9857 annotated vulvoscopy frames from 28 cases.
- This AI tool could improve diagnostic accuracy and reduce invasive procedures in vulvar lesion diagnosis.

## Abstract

Background/Objectives: Accurate identification of vulvar high-grade squamous intraepithelial lesions (HSIL) is essential for preventing progression to invasive squamous cell carcinoma. This study addresses the gap in artificial intelligence (AI) applications for vulvar lesion diagnosis by developing and validating the first convolutional neural network (CNN) model to automatically detect and classify HPV-related vulvar lesions—specifically HSIL and low-grade squamous intraepithelial lesions (LSIL)—based on vulvoscopy images. Methods: This bicentric study included data from 28 vulvoscopies, comprising a total of 9857 annotated frames, categorized using histopathological reports (HSIL or LSIL). The dataset was divided into training, validation, and testing sets for development and assessment of a YOLOv11-based object detection model. Results: The CNN demonstrated a recall (sensitivity) of 99.7% and a precision (positive predictive value) of 99.1% for lesion detection and classification. Conclusions: This is the first AI model developed for detecting and classifying HPV-related vulvar lesions. The integration of such models into vulvoscopy could significantly improve diagnostic accuracy and positively impact women’s health by reducing the need for potentially invasive and anatomy-altering procedures.