Impact of Data Synthesis Strategies for the Classification of Craniosynostosis

TL;DR

This study demonstrates that synthetic data generated from various models can effectively train CNNs to classify craniosynostosis with accuracy comparable to clinical data, potentially reducing the need for patient data in diagnosis.

Contribution

It systematically evaluates the use of multiple synthetic data sources for training CNNs in craniosynostosis classification, showing high accuracy without clinical training data.

Findings

Synthetic data achieved over 0.96 accuracy and 0.95 F1-score.

Combining SSM and GAN was most effective.

Multiple data sources improved classification performance.

Abstract

Introduction: Photogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data is rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically. Methods: We test the combinations of three different synthetic data sources: a statistical shape model (SSM), a generative adversarial network (GAN), and image-based principal component analysis for a convolutional neural network (CNN)-based classification of craniosynostosis. The CNN is trained only on synthetic data, but validated and tested on clinical data. Results: The combination of a SSM and a GAN achieved an accuracy of more than 0.96 and a F1-score of more than 0.95 on the unseen test set. The difference to training on clinical data was smaller than 0.01. Including a second image modality improved classification performance for all data sources. Conclusion: Without a single clinical training sample, a CNN was able to classify head deformities as accurate as if it was trained on clinical data. Using multiple data sources was key for a good classification based on synthetic data alone. Synthetic data might play an important future role in the assessment of craniosynostosis.