  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.
