StepSight AI is an intelligent MRI-based analysis system that helps doctors detect ACL (Anterior Cruciate Ligament) injuries faster and more accurately.
It combines artificial intelligence (MRNet model), medical imaging processing, and a clean web interface to support quick diagnosis and save doctors’ time.
Diagnosing ACL injuries manually from MRI scans is:
- Time-consuming ⏳
- Requires high expertise 🧠
- Prone to human error ❌
StepSight AI allows doctors to:
✔ Upload knee MRI scans directly from a web dashboard
✔ Automatically analyze and detect the risk of ACL injury using AI
✔ Get a clear result: Injury Risk / No Injury Detected
✔ Reduce diagnosis time and improve treatment speed
| Feature | Description |
|---|---|
| 🖥 Doctor Dashboard | Secure interface for medical professionals |
| 🧠 AI-Based MRI Analysis | Uses MRNet (PyTorch model) to analyze ACL risk |
| 📤 DICOM/MRI Upload Support | Supports .dcm, .nii, JPEG MRI images |
| ⚡ Real-Time Prediction | Fast injury detection with probability score |
| 🗂 Organized File Storage | Uploaded scans stored in /backend/uploads/ |
| 🛠 Flask API | Serves model inference API endpoints for frontend |
| Layer | Technology |
|---|---|
| Frontend | HTML, CSS, JavaScript |
| Backend | Python (Flask) |
| AI Model | MRNet (PyTorch), NumPy, OpenCV |
| File Processing | pydicom, PIL |
| Environment | Virtualenv / venv |
| Deployment Ready | Render / Railway / Netlify (optional) |
StepSight-AI/ ├── frontend/ │ ├── index.html │ ├── styles.css │ └── script.js │ ├── backend/ │ ├── app.py # Flask backend server │ ├── utils/ # Helper functions (optional) │ ├── models/ # AI model weights (optional) │ ├── uploads/ # Uploaded MRI scans │ ├── results/ # Prediction reports │ └── requirements.txt # Package dependencies │ └── README.md
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git clone https://github.com/Nytester/StepSight-AI.git
cd StepSight-AI
✅ 2. Setup virtual environment
bash
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cd backend
python3 -m venv stepsight-env
source stepsight-env/bin/activate # Mac/Linux
pip install -r requirements.txt
✅ 3. Start the backend server
bash
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python app.py
✅ 4. Open the frontend
Just open this file in your browser:
bash
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frontend/index.html
🌍 API Endpoints (Flask)
Method Endpoint Description
POST /api/v1/mri/upload Upload MRI scan
GET /api/v1/mri/status/<upload_id> Check processing status
GET /api/v1/mri/analyze/<upload_id> Perform AI prediction
GET /api/v1/mri/report/<upload_id> Download result/report
👨💻 Team Members
Name Role
Roshan Bhatta AI Model & Backend Integration
Prabhakar Shrestha Frontend Development
Sumit Shrestha Research & System Design
🚀 Future Enhancements
✅ Doctor & Patient Login System
✅ PDF Report Generation with Prediction & MRI Snapshot
✅ Deploy Backend on Render / Railway
✅ Deploy Frontend on Netlify / Vercel
✅ Add Real MRNet Pretrained ACL Model
📜 License
This project is open-source and free to use for educational purposes.
🌟 Thank You!
If you like this project, don't forget to star ⭐ the repository!