Figure 1 In the same issue of "Nature-Biomedical Engineering", two papers on medical artificial intelligence from Wang Guangyu's team were published; one of them was the cover paper of the current issue
Focusing on the intelligentization of emergency treatment modes for large-scale sudden infectious diseases such as the new crown epidemic, researcher Wang Guangyu and research teams such as Professor Lin Tianxin from Sun Yat-sen Memorial Hospital of Sun Yat-sen University and Professor Li Weimin from West China Hospital have built an X-ray image-based A cross-crowd, cross-clinical intelligent system
Figure 2 Schematic diagram of the deep learning process for the diagnosis and identification of viral, non-viral and COVID-19 pneumonia based on chest X-ray
Chronic kidney disease and diabetes are chronic systemic diseases, and how to effectively prevent them is a major problem in the world's public health
The research results are based on "Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images).
Figure 3 Schematic diagram of the deep learning model for detecting and predicting the risk of chronic kidney disease and type 2 diabetes from retinal fundus images
As the "artificial intelligence-driven new technology and remote high-efficiency prevention and treatment system for major diseases" that supports the above two academic achievements, it has realized the deployment and application of medical scenarios, and was selected as the "World Internet" of the 2021 Wuzhen World Internet Conference.
This article is an English version of an article which is originally in the Chinese language on echemi.com and is provided for information purposes only.
This website makes no representation or warranty of any kind, either expressed or implied, as to the accuracy, completeness ownership or reliability of
the article or any translations thereof. If you have any concerns or complaints relating to the article, please send an email, providing a detailed
description of the concern or complaint, to
service@echemi.com. A staff member will contact you within 5 working days. Once verified, infringing content
will be removed immediately.
