Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal.
WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal.
Medicina (Kaunas). 2023 Jan 15;59(1):172. doi: 10.3390/medicina59010172.
: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn's disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. : A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision-recall curve (AUC-PR). : Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm's accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. : To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.
: 设备辅助式小肠镜检查（DAE）在处理肠内病变方面具有重要作用。观察溃疡或糜烂是常见的，并且可能与许多疾病实体相关，即克罗恩病。尽管人工智能（AI）在各种基于影像学的胃肠病学程序中的应用呈指数级增长，但仍缺乏关于 DAE 的 AI 技术可行性和临床适用性的证据。本研究旨在开发和测试一种基于多品牌卷积神经网络（CNN）的算法，用于自动检测 DAE 中的溃疡和糜烂。 : 进行了一项单中心回顾性研究，以开发一种基于总共 250 次 DAE 检查的 CNN。使用了总共 6772 张图像，其中 678 张在经过双重验证后被认为是溃疡或糜烂。数据分为训练集和验证集，后者用于评估模型的性能。我们的主要观察指标是敏感性、特异性、准确性、阳性预测值（PPV）、阴性预测值（NPV）和曲线下面积精度-召回曲线（AUC-PR）。 : 敏感性、特异性、PPV 和 NPV 分别为 88.5%、99.7%、96.4%和 98.9%。算法的准确率为 98.7%。AUC-PR 为 1.00。CNN 每秒处理 293.6 帧，使 AI 能够在 DAE 中的实际临床环境中实时应用。 : 据我们所知，这是第一项关于在 DAE 期间通过多品牌全内镜自动检测整个消化道溃疡和糜烂的研究，克服了一个相关的互操作性挑战。我们的结果表明，使用 CNN 检测这种类型的病变与高总体准确性相关。开发用于自动检测临床相关内镜发现和评估内镜炎症活动的二进制 CNN 是 AI 在消化内镜中应用的重要步骤，特别是用于全内镜评估。
