Quantification of Head Shape from Three-Dimensional Photography for Presurgical and Postsurgical Evaluation of Craniosynostosis.

ABSTRACT

BACKGROUND: Evaluation of surgical treatment for craniosynostosis is typically based on subjective visual assessment or simple clinical metrics of cranial shape that are prone to interobserver variability. Three-dimensional photography provides cheap and noninvasive information to assess surgical outcomes, but there are no clinical tools to analyze it. The authors aim to objectively and automatically quantify head shape from three-dimensional photography. METHODS: The authors present an automatic method to quantify intuitive metrics of local head shape from three-dimensional photography using a normative statistical head shape model built from 201 subjects. The authors use these metrics together with a machine learning classifier to distinguish between patients with (n = 266) and without (n = 201) craniosynostosis (aged 0 to 6 years). The authors also use their algorithms to quantify objectively local surgical head shape improvements on 18 patients with presurgical and postsurgical three-dimensional photographs. RESULTS: The authors' methods detected craniosynostosis automatically with 94.74 percent sensitivity and 96.02 percent specificity. Within the data set of patients with craniosynostosis, the authors identified correctly the fused sutures with 99.51 percent sensitivity and 99.13 percent specificity. When the authors compared quantitatively the presurgical and postsurgical head shapes of patients with craniosynostosis, they obtained a significant reduction of head shape abnormalities (p < 0.05), in agreement with the treatment approach and the clinical observations. CONCLUSIONS: Quantitative head shape analysis and three-dimensional photography provide an accurate and objective tool to screen for head shape abnormalities at low cost and avoiding imaging with radiation and/or sedation. The authors' automatic quantitative framework allows for the evaluation of surgical outcomes and has the potential to detect relapses. CLINICAL QUESTION/LEVEL OF EVIDENCE Diagnostic, I.