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A Deep-Learning-Based Collaborative Edge-Cloud Telemedicine System for Retinopathy of Prematurity
Zeliang Luo1, Xiaoxuan Ding2, Ning Hou2
1College of Electro-Mechanical Engineering, Zhuhai City Polytechnic, Zhuhai 519090, China.
Insights
A new telemedicine system uses artificial intelligence to screen premature infants for retinopathy of prematurity. This deep learning approach improves diagnosis in remote areas, reducing blindness risk.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants.
- Timely fundus screening is crucial for ROP diagnosis and treatment.
- Remote areas often lack expert medical staff for effective ROP screening.
Purpose of the Study:
- To develop a deep learning-based collaborative edge-cloud telemedicine system for ROP screening.
- To address the challenges of ROP diagnosis in underserved remote regions.
- To improve access to timely and effective ROP screening for premature infants.
Main Methods:
- A ResNet101 deep learning model was employed for image classification.
- Undersampling and resampling techniques were used to handle data imbalance.
- A collaborative edge-cloud architecture was implemented for a comprehensive telemedicine system.
- The system was embedded in a mobile terminal and deployed in Guangdong Province.
Main Results:
- The system achieved 75% accuracy (ACC) and 60% area under the curve (AUC).
- The AI-powered telemedicine system demonstrated feasibility for remote ROP screening.
- The solution addresses the shortage of medical expertise in rural areas.
Conclusions:
- The proposed deep learning telemedicine system offers a viable solution for ROP screening in remote areas.
- This technology can significantly mitigate the impact of uneven medical resource distribution.
- Further development and deployment can improve outcomes for premature infants at risk of ROP.
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.
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