Machine learning-based identification of craniosynostosis in newborns

Abstract

Early closure of cranial vault sutures, defined as craniosynostosis is a relatively common condition with somehow specific head and face abnormality for each subtype. Early diagnosis results in a better prognosis but pediatricians and primary care providers are not so familiar with these abnormalities while taking 3D CT scan of skull, predisposes the growing brain to harmful effects of radiation. Thus, developing a user-friendly and accurate diagnostic system would be helpful. This study aimed to diagnose simple suture synostosis by using machine learning based methods in digital photographs of child head. Digital photos of 145 craniosynostosis infants, operated in Mofid children hospital (Tehran, Iran) are used in this study. Head border is identified by GrabCut algorithm segmentation method and then several anthropometric indices such as cranial index (CI), cranial vault asymmetry index (CVAI), anterior–midline width ratio (AMWR) and anterior–posterior​ width ratio (APWR) and left–right height ratio (LRHR) are calculated. Moreover, statistical pattern matching indices (Chi-square (CS), Hu moment invariants (HuMI), absolute difference of white pixels probability (AbsDifWPP) and pixel intensity (PI)) are calculated and compared to anthropometric indices. The classification results for statistical pattern matching indices varied in the range of 85%–92% which is statistically higher than hand crafted indices. Our proposed approach could diagnose and classify common subtypes of single suture craniosynostosis and could help pediatricians and parents in early diagnosis and follow-up of this disorder.

Keywords

• Craniosynostosis
• Statistical pattern matching
• Anthropometric indices