用深度学习实现糖尿病视网膜病变自动筛查，提升偏远地区诊疗可及性。
PerceptronCARE: A Deep Learning-Based Intelligent Teleophthalmology Application for Diabetic Retinopathy Diagnosis
- 基于ResNet-18等CNN模型，构建轻量高效诊断系统
- 疾病分级准确率达85.4%，支持实时临床应用
- 集成云端部署与安全数据管理，适合基层医疗推广
糖尿病视网膜病变是成人失明的主要原因，尤其在资源匮乏地区构成重大健康挑战。本研究提出PerceptronCARE，一种基于深度学习的远程眼科学应用，通过眼底图像实现糖尿病视网膜病变的自动化检测。系统采用ResNet-18、EfficientNet-B0和SqueezeNet等多种卷积神经网络进行开发与评估，以平衡精度与计算效率。最终模型对疾病严重程度分类准确率达85.4%，支持临床与远程医疗环境下的实时筛查。PerceptronCARE集成云扩展能力、安全患者数据管理及多用户框架，有助于早期诊断、改善医患互动并降低医疗成本。研究表明，人工智能驱动的远程医疗方案在拓展糖尿病视网膜病变筛查覆盖范围方面具有巨大潜力，尤其适用于偏远和资源受限地区。
原文摘要 · Abstract (English)
Diabetic retinopathy is a leading cause of vision loss among adults and a major global health challenge, particularly in underserved regions. This study presents PerceptronCARE, a deep learning-based teleophthalmology application designed for automated diabetic retinopathy detection using retinal images. The system was developed and evaluated using multiple convolutional neural networks, including ResNet-18, EfficientNet-B0, and SqueezeNet, to determine the optimal balance between accuracy and computational efficiency. The final model classifies disease severity with an accuracy of 85.4%, enabling real-time screening in clinical and telemedicine settings. PerceptronCARE integrates cloud-based scalability, secure patient data management, and a multi-user framework, facilitating early diagnosis, improving doctor-patient interactions, and reducing healthcare costs. This study highlights the potential of AI-driven telemedicine solutions in expanding access to diabetic retinopathy screening, particularly in remote and resource-constrained environments.
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