Neurosurgeries made safe!
SurgeVue is a pioneering augmented reality (AR) surgical assistant tailored for neurosurgeons. By integrating advanced technologies, SurgeVue redefines the surgical experience, enhancing precision and safety during brain tumor removal. Our mission is to equip surgeons with the tools they need to perform with utmost confidence, ultimately improving patient outcomes in one of the most intricate fields of medicine.
The journey to create SurgeVue was fueled by a profound understanding of the risks associated with brain surgeries. With the mortality rate for elective neurosurgery rising dramatically, we aimed to develop a solution that mitigates these risks. Our vision combines the power of augmented reality and machine learning to provide real-time insights, allowing surgeons to visualize complex data and make informed decisions during critical operations.
SurgeVue offers a comprehensive suite of features designed to enhance the surgical process:
- Real-time AR Overlays: Tumor outlines and critical patient data are displayed directly onto the surgical field, aiding in precision during procedures.
- Machine Learning Integration: Our algorithms classify tumors and detect foreign objects, ensuring a thorough surgical environment.
- Instrument Tracking: An integrated Arduino gyroscope monitors surgical instruments, providing feedback on their positioning and movement.
- Security Features: Utilizing facial recognition and RFID technology, SurgeVue safeguards patient records and ensures only authorized personnel can access sensitive data.
SurgeVue is the result of collaborative innovation, employing a variety of technologies:
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Languages:
- Python for backend processing
- Swift for iOS app development
- C++ for performance-critical components
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Frameworks:
- PyTorch: Machine learning model development
- OpenCV: Image processing for tumor detection
- Flask: Backend API server for real-time data handling
- SceneKit: Augmented reality environment creation
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Platforms:
- iOS for mobile accessibility
- Arduino for hardware integration
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Cloud Services:
- Google Cloud for scalable model training and data processing
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APIs:
- PropelAuth for secure authentication and access control
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Hardware:
- Arduino gyroscope for instrument tracking
- RFID sensors for secure authentication
The path to creating SurgeVue was not without its hurdles:
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Latency Issues: Achieving seamless synchronization between AR overlays and real-time surgical environments was challenging. We focused on optimizing performance to minimize lag.
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Model Accuracy: Training our machine learning models required extensive data and computational resources. Ensuring high precision while maintaining speed was critical.
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User Interface Design: Crafting an AR interface that is both informative and unobtrusive proved to be a significant design challenge. We aimed to balance functionality with user experience.
We take pride in several milestones achieved throughout the development of SurgeVue:
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Successful Integration: Merging AR technology with real-time medical data has created a tool that enhances surgical accuracy significantly.
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Advanced Machine Learning Models: We developed a highly accurate tumor detection model that operates efficiently in real-time environments.
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User-Friendly Design: Our interface design provides real-time guidance without distracting surgeons, promoting a seamless surgical experience.
The creation of SurgeVue offered profound insights into the intersection of technology and medicine:
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Integration Techniques: We learned effective strategies for incorporating machine learning models into AR applications.
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Data Management: Handling large medical datasets taught us about the importance of data integrity and processing speed.
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User-Centered Design: We gained valuable experience in designing interfaces for high-stakes environments, ensuring usability and accessibility.
The journey doesn't end here! Our future plans for SurgeVue include:
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Clinical Trials: Testing our system in real surgical settings to gather valuable feedback and refine our technology.
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Model Expansion: Enhancing our machine learning capabilities to detect a broader spectrum of medical conditions.
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Feature Development: Incorporating advanced surgical planning tools and expanding support for various AR platforms.
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Hardware Integration: Improving the functionality of our instrument tracking system with more sophisticated sensors.
Languages: Python Swift C++
Frameworks: PyTorch OpenCV Flask SceneKit
Platforms: iOS Arduino
Cloud Services: Google Cloud
APIs: PropelAuth
Hardware: Arduino gyroscope RFID sensors
Check out our demo video here.
Explore our codebase: GitHub Repo
- Jeet Dekivadia - Project Organization Lead - jeet.university@gmail.com
- Abhijay Salvi - Computer Vision Lead- salviaj152@gmail.com
- Ved Borade - Hardware and UI Lead- vedmborade@gmail.com
- Aditya Jain - Flask and Integration Lead - aditya.jain2702@gmail.com
Join us in revolutionizing the field of neurosurgery!