Department of Gastroenterology, Shanxi Provincial People's Hospital of Shanxi Medical University, Taiyuan, China.
Department of Gastroenterology, Shanxi Provincial People's Hospital, Taiyuan, China.
Dig Liver Dis. 2020 May;52(5):566-572. doi: 10.1016/j.dld.2019.12.146. Epub 2020 Feb 13.
The sensitivity of endoscopy in diagnosing chronic atrophic gastritis is only 42%, and multipoint biopsy, despite being more accurate, is not always available.
This study aimed to construct a convolutional neural network to improve the diagnostic rate of chronic atrophic gastritis.
We collected 5470 images of the gastric antrums of 1699 patients and labeled them with their pathological findings. Of these, 3042 images depicted atrophic gastritis and 2428 did not. We designed and trained a convolutional neural network-chronic atrophic gastritis model to diagnose atrophic gastritis accurately, verified by five-fold cross-validation. Moreover, the diagnoses of the deep learning model were compared with those of three experts.
The diagnostic accuracy, sensitivity, and specificity of the convolutional neural network-chronic atrophic gastritis model in diagnosing atrophic gastritis were 0.942, 0.945, and 0.940, respectively, which were higher than those of the experts. The detection rates of mild, moderate, and severe atrophic gastritis were 93%, 95%, and 99%, respectively.
Chronic atrophic gastritis could be diagnosed by gastroscopic images using the convolutional neural network-chronic atrophic gastritis model. This may greatly reduce the burden on endoscopy physicians, simplify diagnostic routines, and reduce costs for doctors and patients.
内镜诊断慢性萎缩性胃炎的敏感性仅为 42%，多点活检虽然更准确，但并非总是可行。
本研究旨在构建卷积神经网络以提高慢性萎缩性胃炎的诊断率。
我们收集了 1699 名患者的胃窦部 5470 张图像，并对其病理发现进行了标记。其中 3042 张图像显示萎缩性胃炎，2428 张图像未显示。我们设计并训练了一个卷积神经网络-慢性萎缩性胃炎模型，通过五重交叉验证来准确诊断萎缩性胃炎，此外，还将深度学习模型的诊断结果与三位专家的诊断结果进行了比较。
卷积神经网络-慢性萎缩性胃炎模型诊断萎缩性胃炎的准确性、敏感性和特异性分别为 0.942、0.945 和 0.940，均高于专家。轻度、中度和重度萎缩性胃炎的检出率分别为 93%、95%和 99%。
可以使用卷积神经网络-慢性萎缩性胃炎模型通过胃镜图像诊断慢性萎缩性胃炎。这可能会大大减轻内镜医生的负担，简化诊断程序，并降低医生和患者的成本。
