Automated Detection of Retinopathy in Prematurity Using VGG19- Based Deep Learning on Fundus Images

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Key Points

The central aim is to explore the effectiveness of VGG19-based deep learning for detecting retinopathy in premature infants using fundus images.
Utilized VGG19 deep learning architecture for image analysis.
Analyzed fundus images of premature infants to identify signs of retinopathy.
Employed automated detection techniques to enhance accuracy and speed of diagnosis.
Successfully automated detection helps in early identification of retinopathy.
Deep learning model demonstrated high accuracy in interpreting fundus images.
Results support the feasibility of using AI for routine screening in clinical settings.

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Automated detection identifies retinopathy in premature infants using deep learning algorithms, suggesting improved screening methods.

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Narendhar et al. (2026) studied this question.

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