Guo Xin-Meng, Zhao Hong-Ying, Shi Zhong-Yue, Wang Ying, Jin Mu-Lan
Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100000, China.
Sichuan Da Xue Xue Bao Yi Xue Ban. 2021 Mar;52(2):166-169. doi: 10.12182/20210360501.
The incidence of gastric cancer is the highest among all kinds of malignant tumors in China. Because gastric cancer is very hard to identify in its early stage, the early diagnosis rate of gastric cancer in China is relatively low. At present, the pathological diagnosis of gastric cancer mainly depends on the diagnosis of pathologists. However, the gradual improvement of people's living standards and the growing demand for medical and health care have exacerbated the shortage of medical resources, which has become a even more serious problem. Therefore, there is an urgent need for new technologies to help deal with this challenge. In recent years, with the rapid development of artificial intelligence (AI) and digital pathology, AI-aided pathological diagnosis based on convolutional neural network (CNN) as the core technology is showing promises for improving the diagnostic efficiency of gastric cancer. It is also of great significance for the early diagnosis and treatment of the disease and the reduction of its high incidence and mortality. We herein summarize the application and progress of deep-learning CNN in pathological diagnosis of gastric cancer, as well as the existing problems and prospects of future development.
在中国，胃癌的发病率在各类恶性肿瘤中位居首位。由于胃癌在早期很难被识别，中国胃癌的早期诊断率相对较低。目前，胃癌的病理诊断主要依赖病理学家的诊断。然而，人们生活水平的逐步提高以及对医疗卫生保健需求的不断增长，加剧了医疗资源的短缺，这已成为一个更为严重的问题。因此，迫切需要新技术来应对这一挑战。近年来，随着人工智能（AI）和数字病理学的快速发展，以卷积神经网络（CNN）为核心技术的AI辅助病理诊断在提高胃癌诊断效率方面展现出了前景。这对于该疾病的早期诊断和治疗以及降低其高发病率和死亡率也具有重要意义。我们在此总结深度学习CNN在胃癌病理诊断中的应用和进展，以及现存问题和未来发展前景。
