AI 中文总结
针对医疗机构采用AI/DL医学影像分析的资源与专业壁垒，提出OsteoCAD云边协同框架，经墨西哥骨肿瘤分割案例验证，可低门槛实现端到端DL医疗应用。
AI 中文摘要
人工智能（AI）与深度学习（DL）已显著推动医学影像分析发展，但众多医疗机构因计算资源有限、专业知识匮乏难以采用这些技术。为解决这些障碍，我们推出OsteoCAD，这是一款可让临床实践中DL工具普及的模块化电子健康框架。OsteoCAD通过集成且易用的界面提供端到端的DL能力，涵盖数据集创建、预处理、模型训练及推理。为缓解本地硬件限制，该框架可安全连接至远程GPU基础设施。我们通过墨西哥一项针对大型骨肿瘤分割的真实案例研究验证OsteoCAD的可行性，结果表明该框架无需高级技术专业知识或复杂本地配置，即可实现DL驱动的电子健康解决方案。
英文摘要
Artificial Intelligence (AI) and Deep Learning (DL) have notably advanced medical image analysis, yet many health- care organizations struggle to adopt them due to limited com- putational resources and specialized expertise. To address these barriers, we introduce OsteoCAD, a modular eHealth framework that democratizes access to DL tools in clinical practice. Osteo- CAD delivers end-to-end DL capabilities-from dataset creation and preprocessing to model training and inference-through an integrated and user-friendly interface. To mitigate local hardware constraints, the framework securely connects to remote GPU infrastructures. We validate OsteoCAD's feasibility through a real-world case study in Mexico focused on large bone tumor segmentation. The results demonstrate the framework's ability to enable DL-powered eHealth solutions without demanding ad- vanced technical expertise or complex local configurations.
