人工智能与设备辅助式小肠镜检查术：应用卷积神经网络自动检测肠凸起性病变
Artificial Intelligence and Device-Assisted Enteroscopy: Automatic Detection of Enteric Protruding Lesions Using a Convolutional Neural Network.
机构信息
Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427, Porto, Portugal.
WGO Gastroenterology and Hepatology Training Center, Porto, Portugal.
出版信息
Clin Transl Gastroenterol. 2022 Aug 1;13(8):e00514. doi: 10.14309/ctg.0000000000000514. Epub 2022 Jul 20.
INTRODUCTION
Device-assisted enteroscopy (DAE) plays a major role in the investigation and endoscopic treatment of small bowel diseases. Recently, the implementation of artificial intelligence (AI) algorithms to gastroenterology has been the focus of great interest. Our aim was to develop an AI model for the automatic detection of protruding lesions in DAE images.
METHODS
A deep learning algorithm based on a convolutional neural network was designed. Each frame was evaluated for the presence of enteric protruding lesions. The area under the curve, sensitivity, specificity, and positive and negative predictive values were used to assess the performance of the convolutional neural network.
RESULTS
A total of 7,925 images from 72 patients were included. Our model had a sensitivity and specificity of 97.0% and 97.4%, respectively. The area under the curve was 1.00.
DISCUSSION
Our model was able to efficiently detect enteric protruding lesions. The development of AI tools may enhance the diagnostic capacity of deep enteroscopy techniques.
简介
设备辅助式小肠镜检查（DAE）在小肠疾病的检查和内镜治疗中发挥着重要作用。最近，人工智能（AI）算法在胃肠病学中的应用成为了研究的热点。我们的目的是开发一种 AI 模型，用于自动检测 DAE 图像中的突出性病变。
方法
设计了一种基于卷积神经网络的深度学习算法。对每个帧进行评估，以确定是否存在肠突出性病变。使用曲线下面积、敏感性、特异性、阳性和阴性预测值来评估卷积神经网络的性能。
结果
共纳入了 72 名患者的 7925 张图像。我们的模型具有 97.0%的敏感性和 97.4%的特异性，曲线下面积为 1.00。
讨论
我们的模型能够有效地检测肠突出性病变。人工智能工具的开发可能会增强深肠内镜技术的诊断能力。
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