Chundi Ramesh, G Sasikala, Basivi Praveen Kumar, Tippana Anitha, Hulipalled Vishwanath R, N Prabakaran, Simha Jay B, Kim Chang Woo, Kakani Vijay, Pasupuleti Visweswara Rao
School of Computer Applications, Dayananda Sagar University, Bangalore, India.
School of Computer Science and Applications, REVA University, Rukmini Knowledge Park, Bangalore 560064, India.
Comput Biol Med. 2025 Feb;185:109537. doi: 10.1016/j.compbiomed.2024.109537. Epub 2024 Dec 12.
Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals' well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects.
糖尿病的早期诊断和治疗计划的及时启动对于确保个人健康至关重要。人工智能（AI）和计算机视觉等新兴技术因其能够增强大型数据集的可访问性以进行动态训练，并提供高效的实时智能技术和可预测模型而备受推崇。将AI和计算机视觉技术应用于增强临床数据分析被称为电子健康解决方案，其采用先进方法辅助医疗应用。本研究考察了机器学习、深度学习和机器视觉在全球认知方面的若干进展和应用，重点关注可持续性。本文讨论了利用人工智能和计算机视觉检测糖尿病的重要性，因为它有可能显著减轻对人类生命的危害。本文针对该技术在糖尿病领域的应用提出了一些应对挑战的意见和建议。本研究探讨了采用工业4.0技术（包括机器学习、深度学习和计算机视觉机器人技术）作为有效应对糖尿病相关问题的有效工具的潜力。
