---
license: mit
tags:
- keras
- teeth-alignment
- dental
- healthcare
- unsupervised-learning
- rlhf
- image-classification
datasets:
- custom
library_name: keras
language: en
pipeline_tag: image-classification
---
๐ฆท Teeth Alignment Detection Model
## ๐ง 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.
๐งช **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.
> ๐ This model is a research tool and not a substitute for professional dental evaluation.
---
## ๐๏ธ 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
๐ผ๏ธ **Input shape**: `(224, 224, 3)`
๐ค **Output**: Class probabilities (e.g., `[0.8, 0.2]` โ "aligned")
---
## ๐งพ 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](https://bmcoralhealth.biomedcentral.com/articles/10.1186/s12903-022-02466-x) and [PMC Orthodontic AI](https://pmc.ncbi.nlm.nih.gov/articles/PMC8813223/).
---
## ๐ Usage
### ๐ง Install Dependencies
```bash
pip install tensorflow huggingface_hub
```
### ๐ Load and Predict
```python
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 โ "misaligned"
```
---
## ๐ฅ Input & ๐ค Output
| Type | Description |
| ------ | ------------------------------------------- |
| Input | JPG/PNG image of teeth (224ร224), RGB |
| Output | Class probabilities for alignment detection |
---
## ๐ Performance
While no official metrics are available, CNN models for orthodontic imaging tasks have reported:
* ~95โ98% accuracy ([BMC Oral Health, 2022](https://bmcoralhealth.biomedcentral.com/articles/10.1186/s12903-022-02466-x))
* High F1-scores in clinical benchmarks
**Note:** Performance may vary on images that differ from the training distribution.
---
## โ ๏ธ Limitations
* Not suitable for diagnostic use without expert supervision
* Trained on specific dental image styles โ 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.
---
## ๐ License
๐ชช MIT License โ free to use, modify, and distribute.
[View on Hugging Face โ](https://huggingface.co/AP6621/teeth_alignment_detection_modal)
---
## ๐ References
* [Deep Learning for Orthodontic Photo Classification โ BMC Oral Health](https://bmcoralhealth.biomedcentral.com/articles/10.1186/s12903-022-02466-x)
* [AI for Classifying Orthodontic Images โ PMC Study](https://pmc.ncbi.nlm.nih.gov/articles/PMC8813223/)
* [OpenAI โ Learning from Human Feedback (RLHF)](https://openai.com/research/learning-from-human-feedback)
---
๐ง *Model built and maintained by [VilaVision](https://github.com/VilaVision)*