Multi-Height Extraction of Clinical Parameters Improves Classification of Craniosynostosis

Introduction: 3D surface scan-based diagnosis of craniosynostosis is a promising radiation-free alternative to traditional diagnosis using computed tomography. The cranial index (CI) and the cranial vault asymmetry index (CVAI) are well-established clinical parameters that are widely used. However, they also have the benefit of being easily adaptable for automatic diagnosis without the need of extensive preprocessing.
Methods: We propose a multi-height-based classification approach that uses CI and CVAI in different height layers and compare it to the initial approach using only one layer. We use ten-fold cross-validation and test seven different classifiers. The dataset of 504 patients consists of three types of craniosynostosis and a control group consisting of healthy and non-synostotic subjects.
Results: The multi-height-based approach improved classification for all classifiers. The k-nearest neighbors classifier scored best with a mean accuracy of 89 % and a mean F1-score of 0.75.
Conclusion: Taking height into account is beneficial for the classification. Based on accepted and widely used clinical parameters, this might be a step towards an easy-to-understand and transparent classification approach for both physicians and patients.