A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/276 |
| _version_ | 1797431206164299776 |
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| author | Zeliang Luo Xiaoxuan Ding Ning Hou Jiafu Wan |
| author_facet | Zeliang Luo Xiaoxuan Ding Ning Hou Jiafu Wan |
| author_sort | Zeliang Luo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T09:40:37Z |
| format | Article |
| id | doaj.art-403dac858cd84b61bc0223b8e9419e12 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:40:37Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-403dac858cd84b61bc0223b8e9419e122023-12-02T00:55:02ZengMDPI AGSensors1424-82202022-12-0123127610.3390/s23010276A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of PrematurityZeliang Luo0Xiaoxuan Ding1Ning Hou2Jiafu Wan3College of Electro-Mechanical Engineering, Zhuhai City Polytechnic, Zhuhai 519090, ChinaGuangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, ChinaGuangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, ChinaGuangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, ChinaRetinopathy 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.https://www.mdpi.com/1424-8220/23/1/276retinopathy of prematurity (ROP)artificial intelligenceedge–cloud collaborationdeep learningobject detectiontelemedicine |
| spellingShingle | Zeliang Luo Xiaoxuan Ding Ning Hou Jiafu Wan A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity Sensors retinopathy of prematurity (ROP) artificial intelligence edge–cloud collaboration deep learning object detection telemedicine |
| title | A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity |
| title_full | A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity |
| title_fullStr | A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity |
| title_full_unstemmed | A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity |
| title_short | A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity |
| title_sort | deep learning based collaborative edge cloud telemedicine system for retinopathy of prematurity |
| topic | retinopathy of prematurity (ROP) artificial intelligence edge–cloud collaboration deep learning object detection telemedicine |
| url | https://www.mdpi.com/1424-8220/23/1/276 |
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