🦷 Teeth Alignment Detection Model
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🧠 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
1 from tensorflow import keras
2 from huggingface_hub import hf_hub_download
3
4 # Download model
5 model_path = hf_hub_download ( repo_id = "VilaVision/dentalmisalignmentdetection" , filename = "final_teeth_model.keras" )
6 model = keras . models . load_model ( model_path )
7
8 # Preprocess image
9 img = keras . preprocessing . image . load_img ( "path/to/teeth_image.jpg" , target_size = ( 224 , 224 ) )
10 x = keras . preprocessing . image . img_to_array ( img ) / 255.0
11 x = x . reshape ( ( 1 , ) + x . shape )
12
13 # Predict
14 preds = model . predict ( x )
15 print ( "Raw output:" , preds )
16 # 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
🧠
Model built and maintained by VilaVision 
