Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks
Diabetic macular edema (DME) stands as a leading cause for vision loss among the working-age population. Anti-vascular endothelial growth factor (VEGF) agents are currently recognized as the first-line treatment. However, a significant portion of patients remain insensitive to anti-VEGF, resulting i...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Photodiagnosis and Photodynamic Therapy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1572100025000122 |
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| author | Xueying Yang Fabao Xu Han Yu Zhongwen Li Xuechen Yu Zhiwen Li Li Zhang Jie Liu Shaopeng Wang Shaopeng Liu Jiaming Hong Jianqiao Li |
| author_facet | Xueying Yang Fabao Xu Han Yu Zhongwen Li Xuechen Yu Zhiwen Li Li Zhang Jie Liu Shaopeng Wang Shaopeng Liu Jiaming Hong Jianqiao Li |
| author_sort | Xueying Yang |
| collection | DOAJ |
| description | Diabetic macular edema (DME) stands as a leading cause for vision loss among the working-age population. Anti-vascular endothelial growth factor (VEGF) agents are currently recognized as the first-line treatment. However, a significant portion of patients remain insensitive to anti-VEGF, resulting in sustained visual impairment. Therefore, it's imperative to predict prognosis and formulate personalized therapeutic regimens. Generative adversarial networks (GANs) have demonstrated remarkably in forecasting prognosis of diseases, yet their performance is still constrained by the limited availability of real-world data and suboptimal image quality, which subsequently impacts the model's outputs. We endeavor to employ preoperative images along with postoperative OCT contours annotated and extracted via LabelMe and OpenCV to train the model in generating postoperative contours of critical OCT structures instead of previous whole retinal morphology, considerably alleviating the difficulty of output phase and diminishing the requisite quantity of training datasets. Our study reveals that the GAN could serve as an auxiliary instrument for ophthalmologists in determining the prognosis of individuals and screening patients with poor responses to anti-VEGF therapy. |
| format | Article |
| id | doaj-art-ef1af5f179164c98bf147093997fc194 |
| institution | Kabale University |
| issn | 1572-1000 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photodiagnosis and Photodynamic Therapy |
| spelling | doaj-art-ef1af5f179164c98bf147093997fc1942025-02-07T04:47:20ZengElsevierPhotodiagnosis and Photodynamic Therapy1572-10002025-04-0152104482Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networksXueying Yang0Fabao Xu1Han Yu2Zhongwen Li3Xuechen Yu4Zhiwen Li5Li Zhang6Jie Liu7Shaopeng Wang8Shaopeng Liu9Jiaming Hong10Jianqiao Li11Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, PR ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, PR ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, PR ChinaNingbo Eye Hospital, Wenzhou Medical University, Ningbo, PR ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, PR ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, PR ChinaDepartment of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR ChinaDepartment of Endocrinology, People's Hospital of Zoucheng, Jining, PR ChinaZibo Central Hospital, Binzhou Medical University, Zibo, Shandong province, PR ChinaSchool of computer science, Guangdong Polytechnic Normal University, Guangzhou, PR China; Correspondences author at: School of computer science, Guangdong Polytechnic Normal University, Guangzhou, PR China.School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, PR China; Correspondences author at: School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, PR China.Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, PR China; Correspondences author at: Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, PR China.Diabetic macular edema (DME) stands as a leading cause for vision loss among the working-age population. Anti-vascular endothelial growth factor (VEGF) agents are currently recognized as the first-line treatment. However, a significant portion of patients remain insensitive to anti-VEGF, resulting in sustained visual impairment. Therefore, it's imperative to predict prognosis and formulate personalized therapeutic regimens. Generative adversarial networks (GANs) have demonstrated remarkably in forecasting prognosis of diseases, yet their performance is still constrained by the limited availability of real-world data and suboptimal image quality, which subsequently impacts the model's outputs. We endeavor to employ preoperative images along with postoperative OCT contours annotated and extracted via LabelMe and OpenCV to train the model in generating postoperative contours of critical OCT structures instead of previous whole retinal morphology, considerably alleviating the difficulty of output phase and diminishing the requisite quantity of training datasets. Our study reveals that the GAN could serve as an auxiliary instrument for ophthalmologists in determining the prognosis of individuals and screening patients with poor responses to anti-VEGF therapy.http://www.sciencedirect.com/science/article/pii/S1572100025000122Diabetic macular edemaGenerative adversarial networksDeep neural networksAnti-vascular endothelial growth factorOptical coherence tomography |
| spellingShingle | Xueying Yang Fabao Xu Han Yu Zhongwen Li Xuechen Yu Zhiwen Li Li Zhang Jie Liu Shaopeng Wang Shaopeng Liu Jiaming Hong Jianqiao Li Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks Photodiagnosis and Photodynamic Therapy Diabetic macular edema Generative adversarial networks Deep neural networks Anti-vascular endothelial growth factor Optical coherence tomography |
| title | Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks |
| title_full | Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks |
| title_fullStr | Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks |
| title_full_unstemmed | Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks |
| title_short | Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks |
| title_sort | prediction of oct contours of short term response to anti vegf treatment for diabetic macular edema using generative adversarial networks |
| topic | Diabetic macular edema Generative adversarial networks Deep neural networks Anti-vascular endothelial growth factor Optical coherence tomography |
| url | http://www.sciencedirect.com/science/article/pii/S1572100025000122 |
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