Digital Dental Screening and Consultation System

A comprehensive, AI-powered dental health platform that enables users to screen dental conditions through image analysis, receive AI-driven recommendations, and connect with nearby dental clinics. This full-stack application integrates machine learning models for accurate diagnosis, a responsive web interface, a cross-platform mobile app, and a robust backend API.

Developed by Hari Patel and Het Patel as a capstone project demonstrating expertise in full-stack development, AI/ML integration, and scalable system architecture.

Features

Core Functionality

• AI-Powered Image Analysis: Upload dental images (normal photos or X-rays) for automated disease detection using custom-trained PyTorch models
• Real-Time Predictions: Instant analysis with confidence scores and personalized dental recommendations
• Clinic Locator: Integrated clinic search using geolocation and external APIs for nearby dental services
• Dental Articles: Curated educational content on oral health and preventive care
• User Authentication: Secure login/signup with JWT-based authentication
• Chatbot Assistance: AI-powered chatbot for dental health queries and guidance

Multi-Platform Support

• Web Application: Responsive React-based interface with modern UI/UX
• Mobile Application: Cross-platform Flutter app for iOS and Android
• Backend API: Scalable FastAPI server deployed on Hugging Face Spaces

Technical Highlights

• Machine Learning: Custom CNN models for dental disease classification (accuracy-focused)
• Cloud Deployment: Backend hosted on Hugging Face for global accessibility
• Database Integration: User management and data persistence
• API Integration: Geopify for location services, external article scraping
• Security: CORS-enabled, secure API endpoints with authentication

Technology Stack

Backend

• Framework: FastAPI (Python)
• Machine Learning: PyTorch, timm (PyTorch Image Models)
• Deployment: Hugging Face Spaces
• Database: SQLite (with potential for PostgreSQL/MySQL scaling)
• Authentication: JWT tokens

Frontend (Web)

• Framework: React 18 with TypeScript
• Routing: React Router
• Styling: CSS Modules with modern design principles
• Build Tool: Vite
• State Management: React Hooks

Mobile

• Framework: Flutter (Dart)
• State Management: Provider pattern
• API Integration: HTTP package
• Platform Support: iOS, Android, Web

Machine Learning

• Models: Custom-trained CNNs for normal dental images and X-ray analysis
• Libraries: PyTorch, torchvision, PIL
• Training Data: Dental image datasets
• Deployment: Model serving via FastAPI

DevOps & Tools

• Version Control: Git
• Containerization: Docker (for backend)
• API Testing: Postman
• Code Quality: ESLint, Prettier

Project Structure

Digital-Dental-Screening-and-Consultation-System/
├── Backend/                 # FastAPI server with ML models
│   ├── app/                 # Main application code
│   ├── models/              # Trained PyTorch models
│   ├── requirements.txt     # Python dependencies
│   └── Dockerfile           # Containerization
├── MobileApp/               # Flutter mobile application
│   ├── dental_care/         # Flutter project
│   └── pubspec.yaml         # Dart dependencies
├── WebApp/                  # React web application
│   ├── dental-care-web/     # Vite React project
│   └── package.json         # Node dependencies
├── Models/                  # Additional model files
├── Notebooks/               # Jupyter notebooks for ML experimentation
├── LICENSE                  # MIT License
└── README.md                # Project documentation

Installation & Setup

Prerequisites

• Python 3.8+
• Node.js 16+
• Flutter SDK
• Git

Backend Setup

1. Navigate to the Backend directory:

   cd Backend

2. Install Python dependencies:

   pip install -r requirements.txt

3. Run the FastAPI server:

   python run.py

4. The API will be available at http://localhost:8001

Web Application Setup

1. Navigate to the WebApp directory:

   cd WebApp/dental-care-web

2. Install Node dependencies:

   npm install

3. Start the development server:

   npm run dev

4. Open http://localhost:5173 in your browser

Mobile Application Setup

1. Navigate to the MobileApp directory:

   cd MobileApp/dental_care

2. Install Flutter dependencies:

   flutter pub get

3. Run on connected device/emulator:

   flutter run

Usage

1. User Registration: Sign up with email and password
2. Image Upload: Upload dental photos or X-rays for analysis
3. AI Analysis: Receive instant predictions with confidence scores
4. Recommendations: Get personalized dental care advice
5. Clinic Search: Find nearby dental clinics using location services
6. Educational Content: Browse articles on oral health
7. Chatbot Support: Ask questions about dental health via AI chatbot

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Development Guidelines

• Follow existing code style and conventions
• Write clear, concise commit messages
• Test your changes thoroughly
• Update documentation as needed

License

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

Contact

Hari Patel - GitHub | LinkedIn

Het Patel - GitHub | LinkedIn

Project Repository: https://github.com/haripatel07/Digital-Dental-Screening-and-Consultation-System

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This project showcases advanced skills in AI/ML, full-stack development, and cross-platform application design. Built with scalability and user experience in mind.