Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital.
School of Biomedical Engineering.
J Clin Gastroenterol. 2024 Oct 1;58(9):937-943. doi: 10.1097/MCG.0000000000001972.
Gastric structure recognition systems have become increasingly necessary for the accurate diagnosis of gastric lesions in capsule endoscopy. Deep learning, especially using transformer models, has shown great potential in the recognition of gastrointestinal (GI) images according to self-attention. This study aims to establish an identification model of capsule endoscopy gastric structures to improve the clinical applicability of deep learning to endoscopic image recognition.
A total of 3343 wireless capsule endoscopy videos collected at Nanfang Hospital between 2011 and 2021 were used for unsupervised pretraining, while 2433 were for training and 118 were for validation. Fifteen upper GI structures were selected for quantifying the examination quality. We also conducted a comparison of the classification performance between the artificial intelligence model and endoscopists by the accuracy, sensitivity, specificity, and positive and negative predictive values.
The transformer-based AI model reached a relatively high level of diagnostic accuracy in gastric structure recognition. Regarding the performance of identifying 15 upper GI structures, the AI model achieved a macroaverage accuracy of 99.6% (95% CI: 99.5-99.7), a macroaverage sensitivity of 96.4% (95% CI: 95.3-97.5), and a macroaverage specificity of 99.8% (95% CI: 99.7-99.9) and achieved a high level of interobserver agreement with endoscopists.
The transformer-based AI model can accurately evaluate the gastric structure information of capsule endoscopy with the same performance as that of endoscopists, which will provide tremendous help for doctors in making a diagnosis from a large number of images and improve the efficiency of examination.
胶囊内镜下胃结构识别系统对于胃病变的准确诊断变得越来越重要。深度学习，特别是利用自注意力的转换器模型，在胃肠道（GI）图像识别方面显示出巨大的潜力。本研究旨在建立一种胶囊内镜胃结构识别模型，以提高深度学习在内镜图像识别中的临床适用性。
本研究共使用了南方医院 2011 年至 2021 年间采集的 3343 段无线胶囊内镜视频进行无监督预训练，其中 2433 段用于训练，118 段用于验证。选择了 15 种上消化道结构来量化检查质量。我们还通过准确性、敏感性、特异性、阳性和阴性预测值比较了人工智能模型和内镜医生的分类性能。
基于转换器的人工智能模型在胃结构识别方面达到了较高的诊断准确性水平。在识别 15 种上消化道结构的性能方面，人工智能模型的宏观平均准确率为 99.6%（95%置信区间：99.5-99.7），宏观平均敏感度为 96.4%（95%置信区间：95.3-97.5），宏观平均特异性为 99.8%（95%置信区间：99.7-99.9），与内镜医生具有高度的观察者间一致性。
基于转换器的人工智能模型可以准确评估胶囊内镜的胃结构信息，其性能与内镜医生相当，这将为医生从大量图像中做出诊断提供巨大帮助，并提高检查效率。
