DentalAI Diagnostics 🦷

An advanced dental disease classification system powered by deep learning that provides real-time analysis and evidence-based recommendations for dental conditions with up to 92% accuracy.

🎯 Overview

DentalAI Diagnostics is a cutting-edge dental imaging analysis platform that leverages state-of-the-art artificial intelligence to analyze dental radiographs and identify potential conditions. Built with TensorFlow and Streamlit, this system offers instant, accurate analysis with detailed recommendations for dental care professionals.

✨ Key Features

Image Analysis

• Real-time Processing: Industry-leading analysis speed (<2 seconds per image)
• High-Resolution Support: Optimized for dental radiographs up to 4K resolution
• Batch Processing: Analyze multiple images simultaneously

Disease Classification

• Multi-condition Detection: Advanced neural network trained to identify:
  • Hypodontia (missing teeth)
  • Mouth Ulcers and Lesions
  • Tooth Discoloration Patterns
  • Dental Caries (cavities)
  • Calculus (tartar) Buildup

Visualization & Analytics

• Interactive Dashboard
  • Real-time confidence scoring with dynamic gauges
  • Condition distribution analysis
  • Symptom correlation matrices
  • Historical trend analysis with time-series charts

Reporting System

• Comprehensive Analysis Reports
  • Detailed condition descriptions with medical references
  • Evidence-based treatment recommendations
  • Confidence metrics and uncertainty quantification
  • Exportable PDF reports for patient records

🧐 Technology Stack

Core Components

• Backend Framework: Python 3.9+
• Deep Learning: TensorFlow 2.8+, Keras
• Frontend: Streamlit 1.12+
• Visualization: Plotly 5.0+

Model Architecture

• Base Model: InceptionV3 (transfer learning)
• Custom Layers: Fine-tuned classification head
• Image Processing: PIL, OpenCV
• Data Pipeline: TensorFlow Data API

📦 Installation

Prerequisites

• Python 3.9 or higher
• CUDA-compatible GPU (recommended)
• 8GB RAM minimum

Setup Steps

1. Clone the repository:

git clone https://github.com/yourusername/DentalAI-Diagnostics.git
cd DentalAI-Diagnostics

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

3. Install dependencies:

pip install -r requirements.txt

4. Download pre-trained models:

python scripts/download_models.py

5. Launch the application:

streamlit run app.py

🧠 Model Architecture

Network Design

The system employs a fine-tuned InceptionV3 architecture with custom modifications:

• Input Layer: 224x224x3 (RGB images)
• Backbone: InceptionV3 pre-trained on ImageNet
• Custom Head:
  • Global Average Pooling
  • Dropout (0.5)
  • Dense Layer (1024, ReLU)
  • Output Layer (5 classes, Softmax)

def build_model():
    inception = InceptionV3(
        input_shape=IMAGE_SIZE + [3],
        weights='imagenet',
        include_top=False
    )
    
    # Freeze base layers
    for layer in inception.layers:
        layer.trainable = False
    
    # Add custom classification head
    x = GlobalAveragePooling2D()(inception.output)
    x = Dropout(0.5)(x)
    x = Dense(1024, activation='relu')(x)
    predictions = Dense(NUM_CLASSES, activation='softmax')(x)
    
    return Model(inputs=inception.input, outputs=predictions)

🚀 Usage Guide

Basic Usage

1. Launch the application
2. Upload dental images through the interface
3. Click "Analyze" to process images
4. Review the comprehensive results

📊 Performance Metrics

Model Performance

• Training Accuracy: 90.5%
• Validation Accuracy: 92.3%
• Inference Speed: 1.8s average

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch:

git checkout -b feature/YourFeature

3. Commit changes:

git commit -m 'Add YourFeature'

4. Push to branch:

git push origin feature/YourFeature

5. Submit a Pull Request

Development Guidelines

• Follow PEP 8 style guide
• Add unit tests for new features
• Update documentation
• Maintain code coverage >90%

👏 Acknowledgments

• Dental research institutions for dataset provision
• Google Research for InceptionV3 architecture
• IIoT Engineers for development support
• Open-source community contributors

⚠️ Disclaimer

This software is intended for research and educational purposes only. It should not be used as a replacement for professional medical diagnosis or treatment. Always consult qualified dental professionals for medical advice.

© 2025 DentalAI Diagnostics | Built with ❤️ by IIoT Engineers