卷积神经网络
医学
炎症性肠病
超声波
人工智能
克罗恩病
人工神经网络
增稠
放射科
计算机科学
内科学
疾病
化学
高分子科学
作者
Dan Carter,Ahmad Albshesh,Carmi Shimon,Batel Segal,Alex Yershov,Uri Kopylov,Adele Meyers,Rafael Y. Brzezinski,Shomron Ben‐Horin,Oshrit Hoffer
出处
期刊:Inflammatory Bowel Diseases
[Oxford University Press]
日期:2023-02-16
卷期号:29 (12): 1901-1906
被引量:15
摘要
Abstract Introduction The use of intestinal ultrasound (IUS) for the diagnosis and follow-up of inflammatory bowel disease is steadily growing. Although access to educational platforms of IUS is feasible, novice ultrasound operators lack experience in performing and interpreting IUS. An artificial intelligence (AI)–based operator supporting system that automatically detects bowel wall inflammation may simplify the use of IUS by less experienced operators. Our aim was to develop and validate an artificial intelligence module that can distinguish bowel wall thickening (a surrogate of bowel inflammation) from normal bowel images of IUS. Methods We used a self-collected image data set to develop and validate a convolutional neural network module that can distinguish bowel wall thickening >3 mm (a surrogate of bowel inflammation) from normal bowel images of IUS. Results The data set consisted of 1008 images, distributed uniformly (50% normal images, 50% abnormal images). Execution of the training phase and the classification phase was performed using 805 and 203 images, respectively. The overall accuracy, sensitivity, and specificity for detection of bowel wall thickening were 90.1%, 86.4%, and 94%, respectively. The network exhibited an average area under the ROC curve of 0.9777 for this task. Conclusions We developed a machine-learning module based on a pretrained convolutional neural network that is highly accurate in the recognition of bowel wall thickening on intestinal ultrasound images in Crohn’s disease. Incorporation of convolutional neural network to IUS may facilitate the use of IUS by inexperienced operators and allow automatized detection of bowel inflammation and standardization of IUS imaging interpretation.
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