Impact of data synthesis strategies for the classification of craniosynostosis

Abstract

IntroductionPhotogrammetric 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 are rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically.MethodsWe tested 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 is validated and tested on clinical data.ResultsThe combination of an SSM and a GAN achieved an accuracy of 0.960 and an F1 score of 0.928 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.ConclusionsWithout a single clinical training sample, a CNN was able to classify head deformities with similar accuracy 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.

Generative Adversarial Networks (GANs) in the Field of Head and Neck Surgery: Current Evidence and Prospects for the Future—A Systematic Review

“…In the study conducted by Schaufelberger et al, 2023 [ 29 ] a CNN (convolutional neural network) was trained to classify the different types of craniosynostosis using only synthetic data generated by different generative models, including GANs. The CNN was able to grade four different types of deformity.…”