SmiloAI is an advanced AI-powered dental health detection system that utilizes deep learning and image analysis to identify early and visible signs of common dental issues. The system is designed to detect oral health problems from clear, full-mouth photographs without requiring X-rays.
Built on a convolutional neural network (CNN) architecture, SmiloAI is trained on labeled dental photographs to recognize visual features associated with various oral health conditions, enabling early detection and preventive care.
SmiloAI detects tooth decay by analyzing:
- Tooth discoloration patterns
- Dark spots on tooth surfaces
- Tiny surface holes and irregularities
Using advanced texture recognition and pixel-wise color analysis, the model distinguishes between healthy enamel and decayed areas, enabling early cavity detection to prevent further enamel erosion and tooth damage.
The AI identifies plaque and tartar formation through:
- Recognition of yellowish or whitish deposits along the gum line
- Color and texture differentiation from clean enamel
- Contrast-based feature extraction and edge analysis
This helps users identify areas requiring better cleaning or professional dental intervention.
SmiloAI detects gingivitis by analyzing:
- Gum color variations
- Changes in gum shape and contour
- Presence of swelling or puffiness
- Signs of bleeding around teeth
The model segments gum areas and measures redness intensity to identify inflammation severity before it progresses to more serious gum disease.
The system analyzes tooth shade and color balance to detect:
- Staining from dietary or lifestyle factors
- Signs of poor hygiene
- Enamel weakening
Using brightness normalization and hue histogram analysis, SmiloAI compares tooth color uniformity across the mouth to identify abnormal discoloration patterns.
SmiloAI identifies orthodontic issues such as:
- Tooth crowding
- Overlapping teeth
- Spacing problems
Through geometric feature mapping and edge detection, the system analyzes tooth symmetry and spacing to provide insights that may prompt users to consult orthodontic specialists.
- Deep Learning Framework: Convolutional Neural Network (CNN)
- Image Processing: Advanced computer vision techniques
- Analysis Methods:
- Texture recognition
- Pixel-wise color analysis
- Contrast-based feature extraction
- Edge detection
- Geometric feature mapping
- Brightness normalization
- Hue histogram analysis
- Early Detection: Identify dental issues before they become severe
- Preventive Care: Enable proactive oral health maintenance
- Pre-Screening: Assess dental health before professional visits
- Educational Tool: Help users understand their oral health status
This project is licensed under the MIT License - see the LICENSE file for details.
Copyright (c) 2025 Aswindra Selvam
Version: 2.0-alpha
Status: Development