---
license: apache-2.0
tags:
  - bone-age
  - medical-imaging
  - radiology
  - pediatrics
  - pytorch
  - pytorch-lightning
datasets:
  - rsna-bone-age
metrics:
  - mae
---

# BoneAge: Pediatric Bone Age Assessment

Predicts skeletal bone age (in months) from pediatric hand/wrist X-rays.

## Model Details

- **Architecture:** ConvNeXt-Tiny (ImageNet-22k pretrained) + sex-aware regression head
- **Input:** 512x512 grayscale hand X-ray + patient sex
- **Output:** Bone age in months + uncertainty estimates
- **Training data:** RSNA Pediatric Bone Age Challenge (12,611 images)
- **Validation MAE:** 7.97 months (single model, no TTA)

## Usage

```bash
pip install git+https://github.com/FlatNineOrg/medilab-boneage.git
boneage predict hand.png --sex male --uncertainty
```

```python
from boneage.config import BoneAgeConfig
from boneage.inference.predictor import Predictor

predictor = Predictor(BoneAgeConfig())  # auto-downloads weights
result = predictor.predict("hand.png", sex=1)
print(f"Bone age: {result['predicted_age_months']:.1f} months")
```

## Training

Trained for 46 epochs on a Tesla P100 GPU (Kaggle). ConvNeXt-Tiny backbone with:
- AdamW optimizer (lr=1e-4, backbone at 0.1x)
- Cosine annealing with 5-epoch warmup
- Mixed precision (fp16)
- Augmentation: rotation, scale, flip, brightness/contrast, Gaussian noise, coarse dropout

## License

Apache 2.0