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.
背景/目的：人乳头瘤病毒（HPV）在宫颈癌中的作用已广为人知，但其对阴道癌的影响同样不容忽视。尽管阴道镜检查可对女性生殖道进行全面评估，但其诊断准确性仍有待提升。人工智能（AI）的整合有望提高阴道镜检查的成本效益，但目前尚无专门用于区分阴道低级别（LSILs）与高级别（HSILs）鳞状上皮内病变的AI模型。本研究旨在开发并验证一种用于区分该区域HPV相关异型增生病变的AI模型。 方法：本研究开发了一种卷积神经网络（CNN）模型，用于在阴道镜（阴道镜检查期间）静态图像中区分HSILs与LSILs。该AI模型基于71例检查中获取的57,250帧图像数据集构建（其中90%用于训练/验证[包含5折交叉验证]，10%用于测试）。模型通过敏感性、特异性、准确度及受试者工作特征曲线下面积（AUROC）进行评估。 结果：在阴道HSIL/LSIL区分任务中，训练/验证阶段CNN模型的平均敏感性、特异性和准确度分别为98.7%（95%CI 96.7–100.0%）、99.1%（95%CI 98.1–100.0%）和98.9%（95%CI 97.9–99.8%），平均AUROC为0.990 ± 0.004。测试阶段模型的敏感性达99.6%，特异性与准确度均为99.7%。 结论：这是全球首个能够实现阴道区域HSIL/LSIL区分的AI模型，展现出卓越且稳健的性能指标。该模型的有效应用为AI驱动的全女性生殖道阴道镜评估开辟了新途径，标志着全球女性健康护理领域的重大进步。
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