Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE’s diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images,n= 36,599) and testing dataset (10% of the images,n= 4066) used to evaluate the model. The CNN’s output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.
设备辅助小肠镜（DAE）能够评估整个胃肠道并识别多发病变。然而，DAE的诊断效能仍有提升空间。卷积神经网络（CNN）是一种适用于图像分析的多层架构人工智能模型，但目前缺乏其在DAE中应用的研究。本研究团队旨在开发一种多设备兼容的CNN模型，用于DAE检查中对临床相关病变进行全内镜检测。研究回顾性分析了两个专科中心完成的338例检查，包括152例单气囊小肠镜（富士胶片公司，葡萄牙波尔图）、172例双气囊小肠镜（奥林巴斯公司，葡萄牙波尔图）和14例电动螺旋小肠镜（奥林巴斯公司，葡萄牙波尔图），共提取40,655张图像。图像被划分为训练数据集（占图像总数90%，n=36,599）和测试数据集（占10%，n=4,066），用于模型训练与评估。将CNN输出结果与专家共识分类进行对比，通过敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、准确率及精确率-召回率曲线下面积（AUC-PR）评估模型性能。该CNN模型表现出88.9%的敏感性、98.9%的特异性、95.8%的PPV、97.1%的NPV、96.8%的准确率以及0.97的AUC-PR。本研究团队成功开发了首个适用于DAE全内镜检查的多设备兼容CNN临床病变检测模型。开发精准的深度学习模型对于提升基于DAE的全内镜检查诊断效能具有重要意义。
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