ibdagib/edgefracture-cxr-fracture-probe

EdgeFracture CXR Fracture Probe

A lightweight linear probe that classifies fractures from CXR Foundation embeddings. Designed for edge deployment on NVIDIA Jetson Orin Nano (8 GB).

Model Description

This is a logistic regression classifier trained on 88,064-dimensional embeddings extracted by Google's CXR Foundation model. It converts CXR Foundation — a chest X-ray foundation model — into a musculoskeletal fracture detector, demonstrating that CXR Foundation's learned representations transfer beyond its original thoracic training domain.

• Architecture: sklearn.linear_model.LogisticRegression with StandardScaler preprocessing and temperature calibration (T=3.49)
• Input: 88,064-dim CXR Foundation embedding vector
• Output: Fracture probability (0–1)
• File format: joblib dict with keys model, scaler, temperature
• Size: ~700 KB

Training Data

Trained on FracAtlas — 4,083 musculoskeletal X-rays (717 fractures, 3,366 normal) across four body regions: hand, leg, hip, and shoulder.

Performance

5-fold stratified cross-validation with bootstrap confidence intervals (1,000 iterations):

Region	n_train	AUC	95% CI
Hand	1,510	0.850	[0.829, 0.873]
Leg	2,237	0.888	[0.859, 0.914]
Hip	179	0.864	[0.764, 0.953]*
Shoulder	98	0.848	[0.664, 0.972]*
Overall	4,024	0.882	--

* Wide CI due to small sample size — statistically unreliable.

Data Efficiency

The probe achieves strong performance with remarkably few labeled examples, demonstrating that CXR Foundation embeddings carry transferable signal even for out-of-domain musculoskeletal anatomy:

Training examples	AUC
10	0.555
25	0.578
50	0.607
100	0.683
250	0.785
500	0.820
4,024 (full)	0.882

0.820 AUC at just 500 examples suggests viable deployment in data-scarce clinical settings.

Usage

import joblib
import numpy as np

# Load the probe
probe = joblib.load("fracture_probe.joblib")
model = probe["model"]
scaler = probe["scaler"]
temperature = probe["temperature"]

# Given a CXR Foundation embedding vector
# embedding = extract_embedding(image)  # shape: (88064,)

embedding_scaled = scaler.transform(embedding.reshape(1, -1))
logit = model.decision_function(embedding_scaled)
probability = 1 / (1 + np.exp(-logit / temperature))

Intended Use

• Primary: Fracture screening triage in resource-limited settings without radiologist coverage
• Deployment target: NVIDIA Jetson Orin Nano (8 GB) or similar edge devices
• NOT intended for: Standalone clinical diagnosis. This is a screening aid — all positive findings require radiologist confirmation.

Limitations

• Trained only on FracAtlas (hand, leg, hip, shoulder). Performance on other body regions is unknown.
• Hip and shoulder results have wide confidence intervals due to small sample sizes (179 and 98 training examples respectively).
• Requires CXR Foundation for embedding extraction, which expects chest X-ray-format input images.
• Not validated in a prospective clinical trial.

Integrity

SHA-256 checksum for fracture_probe.joblib:

5ab04c187564f84cd26dfac5467f2949094f1045efc9b59d2d97260ec3f0dad6

A .sha256 sidecar file is included in this repository for automated verification.

Citation

If you use this model, please cite:

@misc{edgefracture2026,
  title={EdgeFracture: CXR Foundation Fracture Probe for Edge Deployment},
  author={Ibrahim Dagib},
  year={2026},
  url={https://huggingface.co/ibdagib/edgefracture-cxr-fracture-probe}
}

Part of EdgeFracture

This probe is one component of the EdgeFracture pipeline — a two-model fracture triage system combining CXR Foundation embeddings with MedGemma clinical reasoning, built for the Google MedGemma Impact Challenge.