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  NeuroSight

About the Project

🚀 Inspiration

Healthcare diagnostics often require expert analysis, which can be expensive and time-consuming. I wanted to leverage AI to make medical insights more accessible and help users get a preliminary analysis of their MRI/CT scans. The idea of NeuroSight came from the need to combine AI-powered medical analysis with additional health management features.

🛠 How I Built It

• Frontend: Developed using Next.js, styled with TailwindCSS, and enhanced with GSAP & Framer Motion for smooth UI/UX.
  
• Backend: Used Next.js API routes to handle server-side logic, integrating MongoDB for storing user scan history and health data.
  
• AI Integration: Leveraged pre-trained machine learning models through APIs to analyze MRI/CT scans for tumor detection.
  
• Additional Features: Built a Symptom Checker, Nutrition Planner, and Dashboard to provide a complete health management system.
  

💡 Challenges I Faced

• AI Model Integration: Ensuring accurate predictions and seamless API communication.
  
• Handling Large Image Uploads: Optimized image processing for faster performance.
  
• Data Privacy & Security: Implemented secure storage and encryption for sensitive medical data.
  
• Building a User-Friendly UI: Made the interface intuitive while handling complex functionalities.
  

🎯 Key Takeaways

• Improved my skills in full-stack development and AI integration.
  
• Learned to handle large datasets efficiently using MongoDB.
  
• Gained experience in optimizing performance and ensuring security for medical applications.
  

NeuroSight was a challenging yet rewarding project, and I’m excited to refine it further! 🚀

Built With

• mongodb
• next.js
• react
• roboflow
• tailwindcss
• typescript