SenseCare: a research platform for medical image informatics and interactive 3D visualization

Area of Science:

• Medical image computing
• Smart health
• Artificial Intelligence (AI)

Background:

• Increasing demand for intelligent, clinic-oriented medical image computing algorithms and platforms in smart health research.
• Existing platforms have limited support for AI algorithms and clinical applications, hindering translational research.

Purpose of the Study:

• To develop SenseCare, a research platform facilitating translational research in intelligent diagnosis and treatment planning.
• To address limitations in current medical image informatics platforms for AI integration and clinical use.

Main Methods:

• Developed SenseCare platform with features like 3D visualization, web-based access, data synchronization, security, multi-center deployment, and collaborative research support.
• Integrated AI toolkits for image segmentation, registration, lesion/landmark detection across diverse modalities (radiology, pathology).
• Facilitated data annotation and AI model training for customized model development and deployment.

Main Results:

• SenseCare offers AI toolkits for various medical image analysis tasks, supporting radiology to pathology.
• The platform simplifies AI model development and deployment for clinical researchers.
• It supports diverse clinical applications, including diagnosis and surgical planning for lung cancer, liver tumors, and coronary artery disease.

Conclusions:

• SenseCare simplifies AI-based medical image analysis, promoting clinical research.
• The platform has the potential to advance diagnosis and treatment applications across a wide range of diseases.
• SenseCare facilitates translational research by bridging AI capabilities with clinical needs.