Beyond Vision: Unveiling The Potential of AI and IOT in Diabetic Retinopathy Diagnosis

• Published in: 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT) Vol. 1; pp. 183 - 190
• Main Authors: Bhuvanachandran, Binoy Sugatha, Anusha Bhamini, A. M, Brindha, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08.08.2024
• Subjects: Accuracy; Artificial Intelligence; Cloud computing; Diabetes Mellitus; Diabetic retinopathy; health care application; Image analysis; Internet of Things; Medical services; Real-time systems; Reviews
• DOI: 10.1109/ICCPCT61902.2024.10673120

In recent years, the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has shown tremendous potential for transformative advancements in healthcare. Traditional diagnostic methods for DR often fall short in providing timely and accurate assessments, emphasizing the critical need for innovative approaches. With the prevalence of diabetes escalating globally, the need for accurate and timely diagnosis of Diabetic Retinopathy (DR) has become imperative. The proposed study aims to critically examine existing literature, methodologies, and advancements in the utilization of AI algorithms for image analysis and IoT for seamless data connectivity in the scenario of DR diagnosis. The review focuses on recent research efforts that leverage AI for robust image analysis and classification of DR stages, considering the dynamic landscape of medical imaging technologies. Additionally, it delves into studies incorporating IoT to enhance connectivity, facilitate real-time data transfer, and leverage cloud computing for efficient and scalable processing. The synthesis of these technologies holds promise for the development of an intelligent diagnostic system that not only detects DR but also classifies its various stages with precision. This review seeks to uncover gaps in the research by doing a thorough investigation of the current literature, challenges, and opportunities in the current landscape of AI and IoT applications in diabetic retinopathy diagnosis. The exploration of methodologies, advantages, and limitations in the proposed intelligent systems will contribute to a better grasp of the state-of-the-art in this evolving field. Ultimately, this study seeks to provide valuable insights for researchers, clinicians, and policymakers, fostering advancements in the development of effective and accessible tools for the timely detection and treatment of diabetic retinopathy.