🦷 DD.Ai : YOLO-Based OPG Image Analyzer Web App

This is a web-based application that uses a YOLO12s machine learning model to detect potential anomalies in dental panoramic X-ray images.

The application provides a simple, user-friendly interface for uploading an image and viewing the AI-analyzed results with bounding boxes drawn around detected issues.

🚀 Key Features

• AI-Powered Detection: Leverages a fine-tuned YOLOv10 model to identify dental anomalies
• Supported Detections: Trained to detect **Healthy teeth, Infection, Impacted teeth, Broken Down Crown/Root, Fractured teeth and Dental Caries **
• Web Interface: Clean, responsive, and easy-to-use interface built with HTML, Tailwind CSS, and JavaScript
• Backend Server: Lightweight Node.js + Express server for image uploads and processing
• Side-by-Side Comparison: Displays the original and processed images for clear analysis

🛠️ Technology Stack

• Machine Learning: Python, PyTorch, Ultralytics YOLO12s, OpenCV
• Backend: Node.js, Express.js
• Frontend: HTML, Tailwind CSS, JavaScript, SweetAlert2

📂 Dataset

This project was trained on the Dental Radiography for Anomaly Detection dataset available on Kaggle.

🔗 Panoramic Dental Xray Dataset on Kaggle

⚙️ Getting Started

Follow these steps to set up and run the project on your local machine.

Prerequisites

Make sure you have the following installed:

• Git
• Node.js (with npm)
• Python 3.9+

🔧 Setup & Installation

Step 1: Clone the Repository

git clone https://github.com/MustaqueemAlam/dental-detector-app.git
cd dental-detector-app

Step 2: Add Model Files

Place your trained model files into the model_files directory:

dental_yolov12_best.pt
class_mapping.pkl

The final structure should look like this:

/dental-detector-app/
├── 📂 model_files/
│   ├── dental_yolov12_best.pt
│   └── class_mapping.pkl
...

Step 3: Set Up the Python Environment

Create a virtual environment:

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
.\venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r python_inference/requirements.txt

Step 4: Set Up the Node.js Server

# Install Node.js dependencies
npm install

Running the Application

1. Ensure the Python virtual environment is active
2. Start the server:

node server.js

1. Open http://localhost:3000 in your browser

How to Use

1. Navigate to http://localhost:3000
2. Upload a dental panoramic X-ray image
3. Click "Diagnose Image"
4. View results showing original and processed images with detected anomalies

⚠️ Disclaimer

This is an educational project and is not intended for real-world medical diagnosis. The AI model's predictions are not a substitute for professional medical advice from a qualified healthcare provider.