Department of Gastroenterology, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, Shanghai 200025, China.
Technology Platform Department, Jinshan Science & Technology (Group) Co., Ltd., Chongqing 401120, China.
World J Gastroenterol. 2023 Feb 7;29(5):879-889. doi: 10.3748/wjg.v29.i5.879.
Small intestinal vascular malformations (angiodysplasias) are common causes of small intestinal bleeding. While capsule endoscopy has become the primary diagnostic method for angiodysplasia, manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload, which affects the accuracy of diagnosis.
To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine, achieve automatic disease detection, and shorten the capsule endoscopy (CE) reading time.
A convolutional neural network semantic segmentation model with a feature fusion method, which automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour, thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions, was proposed. Resnet-50 was used as the skeleton network to design the fusion mechanism, fuse the shallow and depth features, and classify the images at the pixel level to achieve the segmentation and recognition of vascular dysplasia. The training set and test set were constructed and compared with PSPNet, Deeplab3+, and UperNet.
The test set constructed in the study achieved satisfactory results, where pixel accuracy was 99%, mean intersection over union was 0.69, negative predictive value was 98.74%, and positive predictive value was 94.27%. The model parameter was 46.38 M, the float calculation was 467.2 G, and the time length to segment and recognize a picture was 0.6 s.
Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.
小肠血管畸形（血管发育不良）是小肠出血的常见原因。胶囊内镜已成为血管发育不良的主要诊断方法，但手动阅读整个胃肠道既费时又费力，影响诊断的准确性。
评估人工智能是否可以辅助诊断，提高小肠血管发育不良的检出率，实现疾病的自动检测，并缩短胶囊内镜（CE）的阅读时间。
提出了一种具有特征融合方法的卷积神经网络语义分割模型，该模型可以自动识别 CE 下血管发育不良的类别并绘制病变轮廓，从而提高识别小肠血管畸形病变的效率和准确性。使用 Resnet-50 作为骨架网络设计融合机制，融合浅层和深度特征，并对图像进行像素级分类，以实现血管发育不良的分割和识别。构建了训练集和测试集，并与 PSPNet、Deeplab3+ 和 UperNet 进行了比较。
研究中构建的测试集取得了令人满意的结果，像素准确率为 99%，平均交并比为 0.69，阴性预测值为 98.74%，阳性预测值为 94.27%。模型参数为 46.38 M，浮点计算为 467.2 G，分割和识别一张图片的时间长度为 0.6 s。
基于深度学习构建分割网络来分割和识别血管发育不良病变是诊断血管发育不良病变的一种有效且可行的方法。
