Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal.
Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal.
Medicina (Kaunas). 2023 Apr 21;59(4):810. doi: 10.3390/medicina59040810.
: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. : A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. : Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. : A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.
胶囊内镜（CE）是一种非侵入性的小肠检查方法，与其他内镜方法一样，需要充分的小肠清洁才能获得明确的结果。近年来，人工智能（AI）算法在医学成像领域显示出了重要的优势，特别是通过适应卷积神经网络（CNN）实现更高效的图像分析。在这里，我们旨在开发一种使用 CNN 自动分类 CE 肠道准备质量的深度学习模型。
基于在葡萄牙波尔图的两个临床中心获得的 12950 张 CE 图像设计了一个 CNN。对每张图像的肠道准备质量进行分类，结果为：优秀，>90%的图像表面可见黏膜；满意，50%-90%的黏膜可见；不满意，<50%的黏膜可见。总图像集分为 80:20 的比例，分别建立训练和验证数据集。将 CNN 的预测结果与一组三位 CE 专家的共识分类进行比较，目前共识分类被认为是评估清洁度的金标准。随后，使用独立的验证数据集评估 CNN 在诊断方面的表现。
在所获得的图像中，3633 张被指定为准备不佳，6005 张为准备满意，3312 张为准备优秀。在区分小肠准备类别时，这里开发的算法总体准确率为 92.1%，灵敏度为 88.4%，特异性为 93.6%，阳性预测值为 88.5%，阴性预测值为 93.4%。检测优秀、满意和不满意类别的曲线下面积分别为 0.98、0.95 和 0.99。
开发了一种基于 CNN 的工具来自动分类 CE 的小肠准备情况，结果表明该工具能够准确地分类 CE 的肠道准备情况。此类系统的开发可以提高用于此类目的的量表的重现性。
