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<p>In today’s world, Diabetic Retinopathy (DR) remains a leading cause of vision loss globally, necessitating early detection and accurate diagnosis for timely intervention. Traditional machine learning and deep learning-based approaches, while effective, often suffer from issues such as limited interpretability, static decision-making, and inadequate generalization across diverse patient data. This research introduces an Agentic-AI Driven Framework for Diabetic Retinopathy Analysis (AADR-AI), which leverages intelligent agent-based learning mechanisms to enhance decision-making autonomy, dynamic adaptability, and contextual understanding of retinal fundus images. The novelty lies in incorporating agentic intelligence principles, autonomy, reactivity, and proactivity into DR detection systems, allowing real-time analysis and adaptive feature learning based on patient-specific variations. The proposed AADR-AI framework integrates a multi-agent ensemble of convolutional and transformer-based networks, coordinated through a decision fusion layer for robust classification. Key contributions include improved classification accuracy (up to 96.7%), enhanced model efficiency with reduced computational overhead, and real-time adaptability to varying image qualities and disease progression stages. Extensive experimentation on benchmark datasets demonstrates superior performance compared to existing state-of-the-art methods. This work highlights the transformative potential of agentic AI in medical imaging, paving the way for more autonomous and interpretable clinical decision-support systems.</p>

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Detection and diagnosis of diabetic retinopathy in retinal fundus images using agentic AI approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月26日

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  • R. Sathya,
  • A. Valaramathi

摘要

In today’s world, Diabetic Retinopathy (DR) remains a leading cause of vision loss globally, necessitating early detection and accurate diagnosis for timely intervention. Traditional machine learning and deep learning-based approaches, while effective, often suffer from issues such as limited interpretability, static decision-making, and inadequate generalization across diverse patient data. This research introduces an Agentic-AI Driven Framework for Diabetic Retinopathy Analysis (AADR-AI), which leverages intelligent agent-based learning mechanisms to enhance decision-making autonomy, dynamic adaptability, and contextual understanding of retinal fundus images. The novelty lies in incorporating agentic intelligence principles, autonomy, reactivity, and proactivity into DR detection systems, allowing real-time analysis and adaptive feature learning based on patient-specific variations. The proposed AADR-AI framework integrates a multi-agent ensemble of convolutional and transformer-based networks, coordinated through a decision fusion layer for robust classification. Key contributions include improved classification accuracy (up to 96.7%), enhanced model efficiency with reduced computational overhead, and real-time adaptability to varying image qualities and disease progression stages. Extensive experimentation on benchmark datasets demonstrates superior performance compared to existing state-of-the-art methods. This work highlights the transformative potential of agentic AI in medical imaging, paving the way for more autonomous and interpretable clinical decision-support systems.