“Quantification of head shape from three-dimensional photography for pre- and post-surgical evaluation of craniosynostosis” - Porras, Antonio R. (Plast Reconstr Surg, 2019)
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 inter-observer variability. 3D photography provides cheap and non-invasive information to assess surgical outcomes, but there are no clinical tools to analyze it. We aim to objectively and automatically quantify head shape from 3D photography. METHODS: We present an automatic method to quantify intuitive metrics of local head shape from 3D photography using a normative statistical head shape model built from 201 subjects. We use these metrics together with a machine learning classifier to distinguish between patients with (N=266) and without (N=201) craniosynostosis (age: 0-6 years). We also use our algorithms to quantify objectively local surgical head shape improvements on 18 patients with pre- and post-surgical 3D photographs. RESULTS: Our methods detected craniosynostosis automatically with 94.74% sensitivity and 96.02% specificity. Within the dataset of patients with craniosynostosis, we identified correctly the fused sutures with 99.51% sensitivity and 99.13% specificity. When we compared quantitatively the pre- and post-surgical head shapes of patients with craniosynostosis, we obtained a significant reduction of head shape abnormalities (p<5%), in agreement with the treatment approach and the clinical observations. CONCLUSIONS: Quantitative head shape analysis and 3D photography provide an accurate and objective tool to screen for head shape abnormalities at low cost and avoiding imaging with radiation and/or sedation. Our automatic quantitative framework allows for the evaluation of surgical outcomes and has the potential to detect relapses.