PDF(Pubmed)
Abstract：
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.
摘要：
数字单人胆道镜检查（D-SOC）增强了诊断不确定胆道狭窄（BS）的能力。在D-SOC中使用人工智能（AI）模型的试点研究证明了有希望的结果。我们的小组旨在开发一种卷积神经网络（CNN），用于D-SOC中恶性BS的识别和形态表征。在两个中心（葡萄牙和西班牙）的129个D-SOC考试中，总共使用了84,994张图像来开发CNN。每个图像被分类为正常/良性发现或恶性病变（后者取决于组织病理学结果）。此外,对CNN进行了形态学特征检测评估，包括肿瘤血管和乳头状突起。完整的数据集分为训练和验证数据集。该模型通过其灵敏度进行了评估，特异性，阳性和阴性预测值，接收器操作特性和精确召回曲线(AUROC和AUPRC，分别)。该模型实现了82.9%的总体准确率，83.5%的敏感性和82.4%的特异性，AUROC和AUPRC分别为0.92和0.93。开发的CNN成功地将良性发现与恶性BS区分开。AI工具在D-SOC中的开发和应用有可能显着提高该检查的诊断率，以识别恶性狭窄。
