🦴 Knee Osteoarthritis Risk Assessment System

🔗 Live Deployment: (https://osteo-care-qctw.onrender.com)

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🌟 About the Project

OsteoCare is an AI-driven web application designed to assist in the early detection and severity assessment of knee osteoarthritis. The platform supports two diagnostic modes:

• X-ray Image Classification for Kellgren-Lawrence (KL) grading
• Clinical Questionnaire for risk assessment

This dual-mode system ensures that individuals without access to radiological imaging can still get personalized insights via a simple form.

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🌟 Aim

To build an accessible and intelligent osteoarthritis screening system that can:

• Detect and classify OA severity from X-ray images.
• Offer clinical risk assessment without imaging.
• Provide instant feedback to assist early intervention.

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🗺️ Scope

• Dual diagnostic approach: Image + Questionnaire
• Bilingual interface (English/Hindi)
• Real-time predictions
• Personalized user experience
• Google Drive model integration for efficient deployment

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⚙️ Tech Stack

Layer	Technologies Used
Backend	Python, Flask
Frontend	HTML, CSS, Bootstrap, Jinja2
AI Models	TensorFlow, Keras
Image Proc.	OpenCV
Deployment	Render (Free Tier Hosting)
Storage	Google Drive (for large ML models)

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🚀 Key Features

• 🔬 Deep learning model for grayscale knee X-ray KL grading
• 🧠 ML questionnaire model for risk prediction (Low/Moderate/High)
• 🌐 Language toggle: English ↔ Hindi
• 🔐 Secure dynamic model loading via Google Drive
• 📊 User-friendly results with severity explanation

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🧪 How to Run Locally

⚠️ Python 3.7+ required

1. Clone the Repository

git clone https://github.com/komalg11/osteo-care.git
cd osteo-care

2. Create a Virtual Environment

python -m venv venv
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Flask App

python app.py

5. Open in Browser

http://127.0.0.1:5000

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🌍 Deployment Notes

• Hosted on Render (Free Tier)
• Models are not bundled in the repo due to large size
• Google Drive is used for on-the-fly model loading

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🔒 Security Best Practices

• Use .env to store your SECRET_KEY and sensitive info
• Never hardcode secrets in source files (e.g., app.py)

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📩 Contact

For feedback, suggestions, or collaboration: komalgupta1157@gmail.com