OcanPredict - Smart Web Based Application for Dental Disease Detection using Artificial Intelligence

Overview

OcanPredict is a smart web-based application designed for early screening OSMF (Oral submucous fibrosis) which is a precancerous condition. It can also screen for various dental diseases using the power of Artificial Intelligence. It leverages state-of-the-art machine learning models and computer vision techniques to analyze real-time camera input and provide users with preliminary assessments of several oral health conditions. This tool aims to empower individuals with accessible and convenient preliminary screenings, promoting proactive dental care and early intervention. While not a replacement for professional diagnosis, OcanPredict can serve as an informative tool for raising awareness and encouraging timely consultations with dental professionals

Also It is multilingual supporting English, Hindi and Marathi for local adaptation.

Demo

https://huggingface.co/spaces/piyush3/OcanPredict

Tech Stack & Tools

Frontend

• React.js
• Vite
• TailwindCSS
• TensorFlow.js
• MediaPipe

Development Tools

• Node.js
• npm/yarn
• Git & GitHub

Installation & Setup

1. Clone the repository:

git clone https://github.com/yourusername/OcanPredict-master.git

cd OcanPredict-master

npm install

npm run dev

Features

Core Functionalities

• Real-time facial analysis using TensorFlow.js
• Multiple condition assessment:
  • Oral Cancer Detection
  • Gingivitis Analysis
  • Calculus Detection
  • OSMF Assessment
  • Caries Detection
  • Phenotype Analysis

User Interface

• Responsive design
• Intuitive navigation
• Real-time camera integration
• Detailed results visualization
• Educational information section

Project Structure

OcanPredict/
├── src/                  # Source files
│   ├── components/      # Reusable UI components
│   ├── Pages/          # Main application pages
│   └── assets/         # Static resources
├── public/             # Public assets and models
│   └── models/        # ML models
└── dist/              # Production build

Technical Workflow

1. User Interface Layer

   • React components handle the UI rendering
   • TailwindCSS manages styling
   • Vite provides fast development and optimization

2. ML Processing Layer

   • TensorFlow.js loads and runs ML models
   • Face detection using MediaPipe
   • Real-time analysis and prediction

3. Data Flow

   • Camera input → Face Detection → Feature Extraction → Analysis → Results

Development Setup

1. Install dependencies:

npm install

2. Start development server:

npm run dev

3. Build for production:

npm run build

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

For more information or support, please open an issue in the GitHub repository.