Afonso João, Mascarenhas Miguel, Ribeiro Tiago, Cardoso Hélder, Andrade Patrícia, Ferreira João P S, Saraiva Miguel Mascarenhas, Macedo Guilherme
Department of Gastroenterology, São João University Hospital, Porto, Portugal.
WGO Gastroenterology and Hepatology Training Center, Porto, Portugal.
Gastro Hep Adv. 2022 Apr 18;1(5):835-843. doi: 10.1016/j.gastha.2022.04.008. eCollection 2022.
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.
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.
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.
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.
胶囊内镜（CE）彻底改变了小肠研究，克服了传统内镜的局限性。然而，查看CE图像很耗时。卷积神经网络（CNN）是一种在图像分析方面具有高性能水平的人工智能架构。小肠突出性病变在CE图像中表现出巨大的形态多样性。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性病变。
使用一组包含突出性病变或正常黏膜/其他发现的CE图像开发了一个CNN。共纳入2565例患者。这些图像通过迁移学习插入到一个CNN模型中。我们通过计算其敏感性、特异性、准确性、阳性预测值和阴性预测值来评估该网络的性能。
基于总共21320张CE图像开发了一个CNN。分别构建了由图像总数的80%和20%组成的训练和验证数据集，用于网络的开发和测试。该算法自动检测小肠突出性病变的准确率为97.1%。我们的CNN的敏感性、特异性、阳性和阴性预测值分别为95.9%、97.1%、83.0%和95.7%。该CNN的运行速度约为每秒355帧。
我们开发了一种准确的CNN，用于自动检测各种形态的肠道突出性病变。这些工具的开发可能会提高CE的诊断效率。
