Augmented Reality Training Platform for Medical Anatomy Education

Medical training programs face high costs related to equipment, cadaver use, and expertise transfer, while existing methods such as dissection and 2D imaging have limitations in visualizing complex internal structures. This hampers effective learning, especially for living human anatomy, and restricts access to immersive, interactive learning experiences across multiple locations.

A leading medical educational institution or healthcare organization seeking innovative training solutions to enhance anatomy learning and surgical preparation.

• Develop a scalable augmented reality platform to visualize and interact with 3D models of internal anatomical structures.
• Enable educators to control and manipulate 3D organ models in a shared physical space using hand gestures and device controls.
• Create an accessible solution that allows students to participate remotely through AR headsets, enhancing learning flexibility.
• Build an extensive library of detailed, anatomically accurate 3D organ and system models, including post-surgical and pathological variations.
• Design an intuitive interface for rotating, enlarging, highlighting, and animating organ models during training sessions.

• AR visualization of 3D anatomical models that appear levitating in the user’s physical environment.
• Role-based controls allowing educators to manipulate, add, remove, and animate models during sessions.
• User interaction via hand gestures for rotating, enlarging, highlighting, or viewing through translucent tissues.
• Room-scale environment scanning to accurately place and stabilize organ models in the physical space.
• Library management system for storing, retrieving, and updating 3D models including post-operation and disease-affected variations.
• Remote participation support, enabling multiple users in different locations to access synchronized AR views.

• Medical imaging software for importing and processing CT and MRI scan data.
• Existing anatomical model databases for updating and expanding the library.
• User authentication and role management systems for session control.
• Remote communication frameworks to facilitate multi-location sessions.

• System latency must be below 100 milliseconds to ensure seamless interaction.
• Support for at least 50 concurrent users in a single session without performance degradation.
• Data security and compliance with medical data handling standards.
• High availability and fault tolerance for continuous training sessions.

The implementation of this AR-based training platform aims to significantly reduce operational costs associated with traditional anatomy education, enable remote and scalable learning, and improve student understanding of complex internal structures. Projected outcomes include enhanced learner engagement, increased access to high-quality training, and a more versatile educational environment, aligning with increased mastery and improved clinical preparedness.