UNASSIGNED: A CNN was developed using a pool of CE images containing protruding lesions or normal mucosa/other findings. A total of 2565 patients were included. These images were inserted into a CNN model with transfer learning. We evaluated the performance of the network by calculating its sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.
UNASSIGNED: A CNN was developed based on a total of 21,320 CE images. Training and validation data sets comprising 80% and 20% of the total pool of images, respectively, were constructed for development and testing of the network. The algorithm automatically detected small-bowel protruding lesions with an accuracy of 97.1%. Our CNN had a sensitivity, specificity, positive, and negative predictive values of 95.9%, 97.1%, 83.0%, and 95.7%, respectively. The CNN operated at a rate of approximately 355 frames per second.
UNASSIGNED: We developed an accurate CNN for automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.
■使用包含突出病变或正常粘膜/其他发现的CE图像库开发了CNN。共纳入2565例患者。这些图像被插入到具有迁移学习的CNN模型中。我们通过计算其灵敏度来评估网络的性能,特异性,准确度,正预测值,和阴性预测值。
■CNN是基于总共21,320张CE图像开发的。训练和验证数据集占图像总库的80%和20%,分别,是为网络的开发和测试而构建的。该算法自动检测小肠突出病变,准确率达97.1%。我们的CNN很敏感,特异性,积极的,阴性预测值为95.9%,97.1%,83.0%,和95.7%,分别。CNN以大约每秒355帧的速率运行。
■我们开发了一种精确的CNN,用于自动检测具有广泛形态的肠突出病变。这些工具的开发可以提高CE的诊断效率。