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Pixel Tampering: Does Face Redaction Harm Medical AI Performance?

Eduardo M J M Farina^1,2, Felipe A Matsuoka^3,4, Gustavo Corradi¹

¹Dasa, São Paulo, Brazil.

Journal of Imaging Informatics in Medicine

|December 16, 2025

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This summary is machine-generated.

An open-source face redaction tool for head CTs enhances data security with minimal impact on deep learning (DL) model performance for age prediction. Models trained on redacted data show comparable results on both redacted and non-redacted images.

Area of Science:

Medical Imaging
Artificial Intelligence
Data Privacy

Background:

Balancing data sharing and patient privacy is critical in medical imaging research.
Face redaction tools anonymize head CT scans but may affect deep learning (DL) model performance.
Existing DL models for medical imaging require robust anonymization techniques.

Purpose of the Study:

To evaluate an open-source face redaction tool for head CT scans.
To assess the impact of image redaction on DL model performance for age prediction.
To compare models trained on redacted versus non-redacted data.

Main Methods:

A Kaggle competition crowdsourced age prediction models using 2377 redacted head CT studies for training and 148 for testing.
Top-performing models were evaluated on both redacted and non-redacted test sets.

Keywords:

Age prediction Deep learning Face redaction Head CT Medical imaging Model performance

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Performance was measured using mean absolute error (MAE); statistical significance was assessed using paired t-tests.

Main Results:

The two best models achieved MAEs of 2.8 and 3.4 years on redacted test data.
On non-redacted test data, MAEs increased to 3.2 and 3.8 years, with one model showing a significant performance drop (p=0.038).
No significant difference was found between models on the redacted test set (p=0.610).

Conclusions:

Models trained on redacted head CT data demonstrate minimal performance decline when applied to non-redacted images.
The developed face redaction tool enables secure medical data sharing with limited impact on DL accuracy.
This approach supports enhanced data sharing for medical imaging research while preserving patient privacy.