VR Technology
Blender (3D Modeling)
We used Blender to design and animate detailed 3D models of dental tools, equipment, and the virtual patient. This allowed us to create a realistic and immersive clinic environment that mirrors the layout and functionality of a real-world dental setting. The animations simulate natural interactions, such as mouth movements and tool handling, which are crucial for training dental procedures effectively.
Unity (VR Development)
Unity served as the core platform for building our interactive VR experience. Using the XR Interaction Toolkit and custom input systems, we enabled students to engage directly with the environment—picking up instruments, performing procedures, and receiving real-time feedback. Unity also manages the simulation logic, user interface, and connection to external systems like AI services and databases, making it the backbone of our VR training solution.
Artificial Intelligence Intergration
AI played a key role in bringing intelligence to our system. We trained a custom object detection model using a dataset prepared on Roboflow, and deployed it using a Flask server with OpenCV to analyze dental X-ray images. The model detects conditions such as cavities, then automatically retrieves diagnosis details, required tools, and treatment steps from a custom database. Additionally, we developed an intelligent voice assistant to guide users during procedures. This assistant relies on local speech-to-text and text-to-speech models, allowing seamless voice interaction without needing an internet connectio
Vein Viewer
Design & Hardware Integration
The system is built using a Raspberry Pi, a high-resolution camera, and a compact LCD display to create a portable and affordable vein viewer device. The design aims to enhance visibility of veins under the skin, especially for procedures like intravenous injections. The lightweight and low-cost components were chosen to make the device accessible and easy to replicate in clinical or educational settings.
System Connectivity & Real-Time Display
The camera captures live video of the skin, which is processed on the Raspberry Pi and displayed in real time on the LCD screen. The device is designed to be plug-and-play, requiring minimal setup. It ensures smooth operation and portability, making it suitable for both training and practical use in hospitals or simulation labs.
AI-Powered Vein Detection
Artificial Intelligence enhances the device by automatically detecting and highlighting veins in the live video feed. We trained a computer vision model to recognize vein patterns, which is integrated into the Raspberry Pi using OpenCV. This allows healthcare professionals or students to quickly and accurately locate veins, reducing trial-and-error and improving patient comfort