🦷 Teeth Alignment Detection Model
🧠 Overview
This Keras model classifies dental images into aligned vs. misaligned categories. It is designed to aid dental practitioners and orthodontists by analyzing clinical photos or X-rays and detecting signs of malocclusion, crowding, or improper alignment.
🧪 Training Highlights:
- Unsupervised Learning Phase: Learns visual features from unlabeled dental image data.
- RLHF (Reinforcement Learning with Human Feedback): Fine-tuned using expert-labeled feedback to make the predictions align with real-world diagnoses.
📌 This model is a research tool and not a substitute for professional dental evaluation.
🏗️ Architecture
The model is a Convolutional Neural Network (CNN), built in Keras. It likely includes:
- Convolutional layers (Conv2D + ReLU)
- MaxPooling or AveragePooling layers
- Dense classification layers
- Possibly residual connections for stability
🖼️ Input shape: (224, 224, 3)
📤 Output: Class probabilities (e.g., [0.8, 0.2] → "aligned")
🧾 Training Data
Though the dataset is not publicly available, it likely contains:
- Intraoral or panoramic dental photographs
- Images annotated by human experts
- Unlabeled data used in the unsupervised phase
- Labeled samples used during RLHF fine-tuning
The model is inspired by techniques described in BMC Oral Health, 2022 and PMC Orthodontic AI.
🚀 Usage
🔧 Install Dependencies
pip install tensorflow huggingface_hub
📄 Load and Predict
from tensorflow import keras
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(repo_id="VilaVision/dentalmisalignmentdetection", filename="final_teeth_model.keras")
model = keras.models.load_model(model_path)
# Preprocess image
img = keras.preprocessing.image.load_img("path/to/teeth_image.jpg", target_size=(224, 224))
x = keras.preprocessing.image.img_to_array(img) / 255.0
x = x.reshape((1,) + x.shape)
# Predict
preds = model.predict(x)
print("Raw output:", preds)
# Example: preds[0][0] > 0.5 → "misaligned"
📥 Input & 📤 Output
| Type | Description |
|---|---|
| Input | JPG/PNG image of teeth (224×224), RGB |
| Output | Class probabilities for alignment detection |
📈 Performance
While no official metrics are available, CNN models for orthodontic imaging tasks have reported:
- ~95–98% accuracy (BMC Oral Health, 2022)
- High F1-scores in clinical benchmarks
Note: Performance may vary on images that differ from the training distribution.
⚠️ Limitations
- Not suitable for diagnostic use without expert supervision
- Trained on specific dental image styles — generalization may be limited
- May not perform well on low-quality or occluded images
- Biases in training data may affect outputs
Always consult a licensed orthodontist or dentist before taking action based on model predictions.
📜 License
🪪 MIT License – free to use, modify, and distribute.
📚 References
- Deep Learning for Orthodontic Photo Classification – BMC Oral Health
- AI for Classifying Orthodontic Images – PMC Study
- OpenAI – Learning from Human Feedback (RLHF)
🧠 Model built and maintained by VilaVision
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