PDF(Pubmed)
Abstract：
UNASSIGNED: Capsule endoscopy (CE) revolutionized the study of the small intestine, overcoming the limitations of conventional endoscopy. Nevertheless, reviewing CE images is time-consuming. Convolutional Neural Networks (CNNs) are an artificial intelligence architecture with high performance levels for image analysis. Protruding lesions of the small intestine exhibit enormous morphologic diversity in CE images. We aimed to develop a CNN-based algorithm for automatic detection of varied small-bowel protruding lesions.
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.
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.
摘要：
■胶囊内窥镜检查（CE）彻底改变了小肠的研究，克服了传统内窥镜检查的局限性。然而，查看CE图像非常耗时。卷积神经网络（CNN）是一种具有高性能级别的人工智能体系结构，用于图像分析。小肠突出的病变在CE图像中表现出巨大的形态学多样性。我们旨在开发一种基于CNN的算法，用于自动检测各种小肠突出病变。
■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。
■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。
