Li Xun, Zhang Chenxia, Yao Liwen, Zhang Jun, Zhang Kun, Feng Hui, Yu Honggang
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
Endosc Ultrasound. 2023 Nov-Dec;12(6):465-471. doi: 10.1097/eus.0000000000000029. Epub 2023 Dec 22.
EUS is the most accurate procedure to determine the originating mural layer and subsequently select the treatment of submucosal tumors (SMTs). However, it requires superb technical and cognitive skills. In this study, we propose a system named SMT Master to determine the originating mural layer of SMTs under EUS.
We developed 3 models: deep convolutional neural network (DCNN) 1 for lesion segmentation, DCNN2 for mural layer segmentation, and DCNN3 for the originating mural layer classification. A total of 2721 EUS images from 201 patients were used to train the 3 models. We validated our model internally and externally using 283 images from 26 patients and 172 images from 26 patients, respectively. We applied 368 images from 30 patients for the man-machine contest and used 30 video clips to test the originating mural layer classification.
In the originating mural layer classification task, DCNN3 achieved a classification accuracy of 84.43% and 80.68% at internal and external validations, respectively. In the video test, the accuracy was 80.00%. DCNN1 achieved Dice coefficients of 0.956 and 0.776 for lesion segmentation at internal and external validations, respectively, whereas DCNN2 achieved Dice coefficients of 0.820 and 0.740 at internal and external validations, respectively. The system achieved 90.00% accuracy in classification, which is comparable with that of EUS experts.
Our proposed system has the potential to solve difficulties in determining the originating mural layer of SMTs in EUS procedures, which relieves the EUS learning pressure of physicians.
超声内镜检查（EUS）是确定黏膜下肿瘤（SMT）起源肌层并随后选择治疗方法的最准确的检查手段。然而，它需要高超的技术和认知能力。在本研究中，我们提出了一种名为SMT Master的系统，用于在EUS下确定SMT的起源肌层。
我们开发了3种模型：用于病变分割的深度卷积神经网络（DCNN）1、用于肌层分割的DCNN2和用于起源肌层分类的DCNN3。使用来自201例患者的总共2721张EUS图像来训练这3种模型。我们分别使用来自26例患者的283张图像和来自26例患者的172张图像进行内部和外部验证。我们应用来自30例患者的368张图像进行人机竞赛，并使用30个视频片段测试起源肌层分类。
在起源肌层分类任务中，DCNN3在内部和外部验证中的分类准确率分别达到84.43%和80.68%。在视频测试中，准确率为80.00%。DCNN1在内部和外部验证中病变分割的Dice系数分别为0.956和0.776，而DCNN2在内部和外部验证中的Dice系数分别为0.820和0.740。该系统在分类中的准确率达到90.00%，与EUS专家的准确率相当。
我们提出的系统有可能解决EUS检查中确定SMT起源肌层的困难，这减轻了医生的EUS学习压力。
