BACKGROUND & AIMS: Histological remission (HR) is advocated and considered as a new therapeutic target in ulcerative colitis (UC). Diagnosis of histologic remission currently relies on biopsy; during this process, patients are at risk for bleeding, infection, and post-biopsy fibrosis. In addition, histologic response scoring is complex and time-consuming, and there is heterogeneity among pathologists. Endocytoscopy (EC) is a novel ultra-high magnification endoscopic technique that can provide excellent in vivo assessment of glands. Based on the EC technique, we propose a neural network model that can assess histological disease activity in UC using EC images to address the above issues. The experiment results demonstrate that the proposed method can assist patients in precise treatment and prognostic assessment. METHODS: We construct a neural network model for UC evaluation. A total of 5105 images of 154 intestinal segments from 87 patients undergoing EC treatment at a center in China between March 2022 and March 2023 are scored according to the Geboes score. Subsequently, 103 intestinal segments are used as the training set, 16 intestinal segments are used as the validation set for neural network training, and the remaining 35 intestinal segments are used as the test set to measure the model performance together with the validation set. RESULTS: By treating HR as a negative category and histologic activity as a positive category, the proposed neural network model can achieve an accuracy of 0.9, a specificity of 0.95, a sensitivity of 0.75, and an area under the curve (AUC) of 0.81. CONCLUSION: We develop a specific neural network model that can distinguish histologic remission/activity in EC images of UC, which helps to accelerate clinical histological diagnosis. keywords: ulcerative colitis; Endocytoscopy; Geboes score; neural network.

翻译：背景与目的：组织学缓解（HR）被认为是溃疡性结肠炎（UC）的新治疗目标。目前组织学缓解的诊断依赖活检，此过程中患者存在出血、感染及活检后纤维化的风险。此外，组织学反应评分复杂且耗时，且病理学家之间存在异质性。内镜细胞学（EC）是一种新型超高倍放大内镜技术，可对腺体进行出色的体内评估。基于EC技术，我们提出一种神经网络模型，利用EC图像评估UC的组织学疾病活动度，以解决上述问题。实验结果表明，所提方法可辅助患者进行精准治疗与预后评估。方法：我们构建了用于UC评估的神经网络模型。对2022年3月至2023年3月期间在中国某中心接受EC治疗的87名患者中154段肠段的5105张图像，依据Geboes评分进行评分。随后，103段肠段作为训练集，16段肠段作为验证集用于神经网络训练，剩余35段肠段作为测试集，与验证集共同衡量模型性能。结果：将组织学缓解作为阴性类别、组织学活动度作为阳性类别时，所提神经网络模型的准确率为0.9，特异度为0.95，灵敏度为0.75，曲线下面积（AUC）为0.81。结论：我们开发了一种特异性神经网络模型，可区分UC的EC图像中的组织学缓解/活动度，有助于加速临床组织学诊断。关键词：溃疡性结肠炎；内镜细胞学；Geboes评分；神经网络。