VR/AR Technology in Human Anatomy Teaching and Operation Training

Area of Science:

• Medical Education Technology
• Surgical Simulation
• Medical Imaging

Background:

• Traditional anatomy and surgical teaching methods can lack engagement for medical students.
• Integrating virtual and real anatomical data offers a novel approach to clinical education.

Purpose of the Study:

• To evaluate the application and learning effectiveness of an Augmented Reality/Virtual Reality (AR/VR) system in human anatomy and surgical teaching.
• To assess the impact of specific modules on the accuracy of anatomical structure segmentation in medical imaging.

Main Methods:

• Utilized an AR/VR operation simulation system for evaluating learning effects in 41 medical students.
• Implemented and analyzed the performance of a feature reweighting module within the AR/VR system.
• Assessed segmentation accuracy using the Intersection over Union (IOU) metric for bone structures and ultrasound images.

Main Results:

• The feature reweighting module improved bone structure segmentation accuracy in the AR/VR surgery simulation system (IOU from 79.62% to 83.56%).
• Segmentation accuracy for real human ultrasound image data also increased with the module (IOU from 80.21% to 82.23%).
• Dense convolution and feature reweighting modules enhance network learning of bone structure features in ultrasound images.

Conclusions:

• AR/VR systems significantly enhance medical student engagement and learning initiative in anatomy and surgical training.
• The integration of dense convolution and feature reweighting modules demonstrably improves the performance and accuracy of anatomical structure segmentation in medical imaging.
• AR/VR technology, particularly with advanced modules, shows great promise for advancing clinical teaching and surgical simulation.