High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.
高分辨率肛门镜检查（HRA）在肛门鳞状细胞癌（ASCC）前驱病变的检测与治疗中具有核心作用。人工智能（AI）算法在HRA图像中检测并区分高级别鳞状上皮内病变（HSIL）与低级别鳞状上皮内病变（LSIL）已展现出高效能。本研究旨在开发一种深度学习系统，利用传统及数字肛门镜获取的HRA图像，实现HSIL与LSIL的自动检测与鉴别。基于两个大型中心使用传统及数字HRA系统完成的151例HRA检查，我们构建了一个卷积神经网络（CNN）。研究共纳入57,822张图像，其中28,874张包含HSIL病变，28,948张为LSIL病变。通过部分亚组分析评估了CNN在醋酸染色与卢戈碘染色图像子集、以及肛管治疗后的图像中的性能。测试阶段CNN区分HSIL与LSIL的总体准确率为94.6%，算法的总体敏感性和特异性分别为93.6%和95.7%（AUC值为0.97）。在醋酸染色图像中，HSIL与LSIL的鉴别总体准确率达96.4%；而在卢戈碘染色图像及治疗后图像中，该值分别为96.6%和99.3%。将AI算法引入HRA可提升ASCC前驱病变的早期诊断能力，本系统在传统与数字HRA平台中均表现出良好的适用性。
肿瘤(癌症)患者之家