Wang Guotai, Duan Qi, Shen Tian, Zhang Shaoting
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
SenseTime Research, Shanghai, China.
Front Radiol. 2024 Nov 21;4:1460889. doi: 10.3389/fradi.2024.1460889. eCollection 2024.
Clinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. However, existing research platforms for medical image informatics have limited support for Artificial Intelligence (AI) algorithms and clinical applications.
To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios. It has several appealing functions and features such as advanced 3D visualization, concurrent and efficient web-based access, fast data synchronization and high data security, multi-center deployment, support for collaborative research, etc.
SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. It also facilitates the data annotation and model training processes, which makes it easier for clinical researchers to develop and deploy customized AI models. In addition, it is clinic-oriented and supports various clinical applications such as diagnosis and surgical planning for lung cancer, liver tumor, coronary artery disease, etc. By simplifying AI-based medical image analysis, SenseCare has a potential to promote clinical research in a wide range of disease diagnosis and treatment applications.
智能健康领域的临床研究对支持各种应用的智能且面向临床的医学图像计算算法和平台的需求日益增长。然而，现有的医学图像信息学研究平台对人工智能（AI）算法和临床应用的支持有限。
为此，我们开发了SenseCare研究平台，旨在促进各种临床场景下智能诊断和治疗规划的转化研究。它具有多项吸引人的功能和特性，如先进的3D可视化、基于网络的并发高效访问、快速数据同步和高数据安全性、多中心部署、支持合作研究等。
SenseCare为不同任务提供了一系列AI工具包，包括从放射学到病理学等各种图像模态的图像分割、配准、病变和地标检测。它还简化了数据标注和模型训练过程，使临床研究人员更容易开发和部署定制的AI模型。此外，它以临床为导向，支持各种临床应用，如肺癌、肝肿瘤、冠状动脉疾病等的诊断和手术规划。通过简化基于AI的医学图像分析，SenseCare有潜力推动广泛疾病诊断和治疗应用中的临床研究。
