A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity

Abstract

Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis and treatment are of great significance. Due to the lack of timely and effective fundus screening for premature infants in remote areas, leading to an aggravation of the disease and even blindness, in this paper, a deep learning-based collaborative edge-cloud telemedicine system is proposed to mitigate this issue. In the proposed system, deep learning algorithms are mainly used for classification of processed images. Our algorithm is based on ResNet101 and uses undersampling and resampling to improve the data imbalance problem in the field of medical image processing. Artificial intelligence algorithms are combined with a collaborative edge–cloud architecture to implement a comprehensive telemedicine system to realize timely screening and diagnosis of retinopathy of prematurity in remote areas with shortages or a complete lack of expert medical staff. Finally, the algorithm is successfully embedded in a mobile terminal device and deployed through the support of a core hospital of Guangdong Province. The results show that we achieved 75% ACC and 60% AUC. This research is of great significance for the development of telemedicine systems and aims to mitigate the lack of medical resources and their uneven distribution in rural areas.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

“…After entering the 21 st century, DL algorithms and AI technology have been consistently developed to promote their application in medicine [18] , and many studies have demonstrated that AI technology can aid in the screening, diagnosis, and treatment of diseases. At present, in the field of ophthalmology, AI technology has made remarkable achievements in the study of ocular surface diseases such as dry eye, pterygium, keratitis [19][20][21][22] , retinal vascular diseases such as DR, retinopathy of prematurity (ROP) [23][24][25][26][27][28] , age-related macular degeneration (AMD) [29][30][31] , and glaucoma [32][33][34] . However, few studies have investigated its application in the auxiliary diagnosis of RVO.…”

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“…After entering the 21 st century, DL algorithms and AI technology have been consistently developed to promote their application in medicine [18] , and many studies have demonstrated that AI technology can aid in the screening, diagnosis, and treatment of diseases. At present, in the field of ophthalmology, AI technology has made remarkable achievements in the study of ocular surface diseases such as dry eye, pterygium, keratitis [19][20][21][22] , retinal vascular diseases such as DR, retinopathy of prematurity (ROP) [23][24][25][26][27][28] , age-related macular degeneration (AMD) [29][30][31] , and glaucoma [32][33][34] . However, few studies have investigated its application in the auxiliary diagnosis of RVO.…”

A Mobile Application Based Optimized Out-Patient Emergency Request Model

“…In reality we utilize technological tools of all kinds on a regular basis for multiple purposes (e.g., regulating household appliances and tracking energy consumption), which has contributed to the creation and transmission of tremendous amounts of personal data in seconds. In [4][5][6][7] future studies to expand the scope, range and maneuverability of healthcare technologies are addressed by the use of wireless networking technology.…”