Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.
数字单操作者胆道镜（D-SOC）提升了诊断性质不明的胆道狭窄的能力。先前在D-SOC中应用人工智能模型的研究已显示出良好前景。本研究团队旨在开发一种卷积神经网络，用于在D-SOC中识别恶性胆道狭窄并进行形态学特征分析。研究采用来自葡萄牙和西班牙两个中心的129例D-SOC检查的84,994张图像来构建该卷积神经网络。每张图像被分类为正常/良性发现或恶性病变（后者依据组织病理学结果确定）。此外，该网络还针对肿瘤血管和乳头状突起等形态特征的检测能力进行了评估。完整数据集被划分为训练集和验证集。通过敏感性、特异性、阳性预测值、阴性预测值、准确率以及受试者工作特征曲线下面积和精确率-召回率曲线下面积等指标对模型性能进行评估。该模型总体准确率达82.9%，敏感性为83.5%，特异性为82.4%，其AUROC和AUPRC分别为0.92和0.93。所开发的卷积神经网络能有效区分良性发现与恶性胆道狭窄。将人工智能工具开发应用于D-SOC检查，有望显著提升其对恶性狭窄的诊断效能。
肿瘤(癌症)患者之家