Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image
IntroductionUncorrected refractive errors are a leading cause of preventable vision impairment globally, particularly affecting individuals in low-resource regions where timely diagnosis and screening access remain significant challenges despite the availability of economical treatments.AimThis stud...
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Frontiers Media S.A.
2025-07-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1576958/full |
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| author | Muhammad Syauqie Muhammad Syauqie Harry Patria Sutanto Priyo Hastono Kemal Nazaruddin Siregar Nila Djuwita Farieda Moeloek |
| author_facet | Muhammad Syauqie Muhammad Syauqie Harry Patria Sutanto Priyo Hastono Kemal Nazaruddin Siregar Nila Djuwita Farieda Moeloek |
| author_sort | Muhammad Syauqie |
| collection | DOAJ |
| description | IntroductionUncorrected refractive errors are a leading cause of preventable vision impairment globally, particularly affecting individuals in low-resource regions where timely diagnosis and screening access remain significant challenges despite the availability of economical treatments.AimThis study introduces a novel deep learning-based system for automated refractive error classification using photorefractive images acquired via a standard smartphone camera.MethodsA multi-branch convolutional neural network (CNN) was developed and trained on a dataset of 2,139 corneal images collected from an Indonesian public eye hospital. The model was designed to classify refractive errors into four categories: significant myopia, significant hypermetropia, insignificant refractive error, and not applicable to classified. Grad-CAM visualization was employed to provide insights into the model’s interpretability.ResultsThe 3-branch CNN architecture demonstrated superior performance, achieving an overall test accuracy of 91%, precision of 96%, and recall of 98%, with an area under the curve (AUC) score of 0.9896. Its multi-scale feature extraction pathways were pivotal in effectively addressing overlapping red reflex patterns and subtle variations between classes.ConclusionThis study establishes the feasibility of smartphone-based photorefractive assessment integrated with artificial intelligence for scalable and cost-effective vision screening. By training the CNN model with a real-world dataset representative of Southeast Asian populations, this system offers a reliable solution for early refractive error detection with significant implications for improving accessibility to eye care services in resource-limited settings. |
| format | Article |
| id | doaj-art-2f9b0d1c966149b4b3a5ab73b88dfb22 |
| institution | DOAJ |
| issn | 2624-9898 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| spelling | doaj-art-2f9b0d1c966149b4b3a5ab73b88dfb222025-08-20T03:09:35ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982025-07-01710.3389/fcomp.2025.15769581576958Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone imageMuhammad Syauqie0Muhammad Syauqie1Harry Patria2Sutanto Priyo Hastono3Kemal Nazaruddin Siregar4Nila Djuwita Farieda Moeloek5Department of Ophthalmology, Faculty of Medicine, Universitas Andalas, Padang, IndonesiaDepartment of Biostatistics and Population Studies, Faculty of Public Health, Universitas Indonesia, Depok, IndonesiaStrathclyde Business School, University of Strathclyde, Glasgow, United KingdomDepartment of Biostatistics and Population Studies, Faculty of Public Health, Universitas Indonesia, Depok, IndonesiaDepartment of Biostatistics and Population Studies, Faculty of Public Health, Universitas Indonesia, Depok, IndonesiaDepartment of Ophthalmology, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, IndonesiaIntroductionUncorrected refractive errors are a leading cause of preventable vision impairment globally, particularly affecting individuals in low-resource regions where timely diagnosis and screening access remain significant challenges despite the availability of economical treatments.AimThis study introduces a novel deep learning-based system for automated refractive error classification using photorefractive images acquired via a standard smartphone camera.MethodsA multi-branch convolutional neural network (CNN) was developed and trained on a dataset of 2,139 corneal images collected from an Indonesian public eye hospital. The model was designed to classify refractive errors into four categories: significant myopia, significant hypermetropia, insignificant refractive error, and not applicable to classified. Grad-CAM visualization was employed to provide insights into the model’s interpretability.ResultsThe 3-branch CNN architecture demonstrated superior performance, achieving an overall test accuracy of 91%, precision of 96%, and recall of 98%, with an area under the curve (AUC) score of 0.9896. Its multi-scale feature extraction pathways were pivotal in effectively addressing overlapping red reflex patterns and subtle variations between classes.ConclusionThis study establishes the feasibility of smartphone-based photorefractive assessment integrated with artificial intelligence for scalable and cost-effective vision screening. By training the CNN model with a real-world dataset representative of Southeast Asian populations, this system offers a reliable solution for early refractive error detection with significant implications for improving accessibility to eye care services in resource-limited settings.https://www.frontiersin.org/articles/10.3389/fcomp.2025.1576958/fullrefractive error detectionvision screeningartificial intelligenceconvolutional neural networksmartphonered reflex |
| spellingShingle | Muhammad Syauqie Muhammad Syauqie Harry Patria Sutanto Priyo Hastono Kemal Nazaruddin Siregar Nila Djuwita Farieda Moeloek Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image Frontiers in Computer Science refractive error detection vision screening artificial intelligence convolutional neural network smartphone red reflex |
| title | Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image |
| title_full | Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image |
| title_fullStr | Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image |
| title_full_unstemmed | Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image |
| title_short | Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image |
| title_sort | deep learning for vision screening in resource limited settings development of multi branch cnn for refractive error detection based on smartphone image |
| topic | refractive error detection vision screening artificial intelligence convolutional neural network smartphone red reflex |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1576958/full |
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