Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema
Abstract Diabetic Macular Edema (DME) is a major complication of diabetic retinopathy characterized by fluid accumulation in the macula, leading to vision impairment. The standard treatment involves anti-VEGF (Vascular Endothelial Growth Factor) therapy, but approximately 36% of patients do not resp...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-87290-3 |
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| author | Anwesa Mondal Apurba Nandi Subhasish Pramanik Lakshmi Kanta Mondal |
| author_facet | Anwesa Mondal Apurba Nandi Subhasish Pramanik Lakshmi Kanta Mondal |
| author_sort | Anwesa Mondal |
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| description | Abstract Diabetic Macular Edema (DME) is a major complication of diabetic retinopathy characterized by fluid accumulation in the macula, leading to vision impairment. The standard treatment involves anti-VEGF (Vascular Endothelial Growth Factor) therapy, but approximately 36% of patients do not respond adequately, highlighting the need for more precise predictive models to guide treatment. This study aims to develop a Hybrid Deep Learning model to predict treatment responses in DME patients undergoing anti-VEGF therapy, thereby improving the accuracy of treatment planning and minimizing the unnecessary use of costly anti-VEGF agents. The model integrates both Optical Coherence Tomography (OCT) images and clinical data from 181 patients, including key parameters such as serum VEGFR-2 concentration and the duration of DME. The architecture combines convolutional neural networks (CNNs) for image data with multi-layer perceptron (MLP) for tabular clinical data, allowing for a comprehensive analysis of both data types. These pathways converge into a unified predictive framework designed to enhance the model’s accuracy. This study utilized a Hybrid Deep Learning model that achieved an 85% accuracy, with additional metrics including precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC) confirming its robustness and reliability. The findings suggest that the model accurately predicts patient responses to anti-VEGF therapy, paving the way for more personalized and targeted treatment strategies. This approach has the potential to enhance patient outcomes and minimize unnecessary administration of anti-VEGF agents, thereby optimizing therapeutic interventions in ophthalmology. |
| format | Article |
| id | doaj-art-671b7e042cb74dad9d970fa72050bb1a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-671b7e042cb74dad9d970fa72050bb1a2025-02-09T12:33:13ZengNature PortfolioScientific Reports2045-23222025-02-0115111110.1038/s41598-025-87290-3Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edemaAnwesa Mondal0Apurba Nandi1Subhasish Pramanik2Lakshmi Kanta Mondal3Electrical Engineering Department, Jadavpur UniversityElectronics and Tele-Communication Engineering Department, Jadavpur UniversityDepartment of Ophthalmology, Regional Institute of Ophthalmology, Medical CollegeDepartment of Ophthalmology, Regional Institute of Ophthalmology, Medical CollegeAbstract Diabetic Macular Edema (DME) is a major complication of diabetic retinopathy characterized by fluid accumulation in the macula, leading to vision impairment. The standard treatment involves anti-VEGF (Vascular Endothelial Growth Factor) therapy, but approximately 36% of patients do not respond adequately, highlighting the need for more precise predictive models to guide treatment. This study aims to develop a Hybrid Deep Learning model to predict treatment responses in DME patients undergoing anti-VEGF therapy, thereby improving the accuracy of treatment planning and minimizing the unnecessary use of costly anti-VEGF agents. The model integrates both Optical Coherence Tomography (OCT) images and clinical data from 181 patients, including key parameters such as serum VEGFR-2 concentration and the duration of DME. The architecture combines convolutional neural networks (CNNs) for image data with multi-layer perceptron (MLP) for tabular clinical data, allowing for a comprehensive analysis of both data types. These pathways converge into a unified predictive framework designed to enhance the model’s accuracy. This study utilized a Hybrid Deep Learning model that achieved an 85% accuracy, with additional metrics including precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC) confirming its robustness and reliability. The findings suggest that the model accurately predicts patient responses to anti-VEGF therapy, paving the way for more personalized and targeted treatment strategies. This approach has the potential to enhance patient outcomes and minimize unnecessary administration of anti-VEGF agents, thereby optimizing therapeutic interventions in ophthalmology.https://doi.org/10.1038/s41598-025-87290-3Diabetic Macular Edema (DME)Anti-VEGFHybrid deep learning modelDrug efficacy |
| spellingShingle | Anwesa Mondal Apurba Nandi Subhasish Pramanik Lakshmi Kanta Mondal Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema Scientific Reports Diabetic Macular Edema (DME) Anti-VEGF Hybrid deep learning model Drug efficacy |
| title | Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema |
| title_full | Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema |
| title_fullStr | Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema |
| title_full_unstemmed | Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema |
| title_short | Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema |
| title_sort | application of deep learning algorithm for judicious use of anti vegf in diabetic macular edema |
| topic | Diabetic Macular Edema (DME) Anti-VEGF Hybrid deep learning model Drug efficacy |
| url | https://doi.org/10.1038/s41598-025-87290-3 |
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