Available normative references of [[cranial bone]] development and [[suture fusion]] are incomplete or based on simplified assumptions due to the lack of large [[dataset]]s. Liu et al. presented a fully data-driven normative model that represents the age- and sex-specific variability of [[bone shape]], [[bone thickness]], and [[bone density]] between birth and 10 years of age at every location of the [[calvaria]].

The model was built using a cross-sectional and multi-institutional pediatric computed tomography image dataset with 2068 subjects without cranial pathology (age 0-10 years). They combined principal component analysis and temporal regression to build a statistical model of cranial bone development at every location of the calvaria. They studied the influences of sex on [[cranial bone growth]], and the bone density model allowed quantifying for the first time [[suture fusion]] as a continuous temporal process. They evaluated the predictive accuracy of our model using an independent longitudinal image dataset of 51 subjects.

The model achieved temporal predictive errors of 2.98 ± 0.69 mm, 0.27 ± 0.29 mm, and 76.72 ± 91.50 HU in cranial bone shape, thickness, and mineral density changes, respectively. Significant sex differences were found in [[intracranial volume]] and bone surface areas (P < 0.01). No significant differences were found in the [[cephalic index]], [[bone thickness]], [[mineral density]], or [[suture fusion]].

Liu et al. presented the first pediatric age- and sex-specific statistical reference for local cranial bone [[shape]], [[thickness]], and [[mineral density]] changes. They showed its predictive accuracy using an independent longitudinal dataset, they studied developmental differences associated with sex, and quantified suture fusion as a continuous process
((Liu J, Elkhill C, LeBeau S, French B, Lepore N, Linguraru MG, Porras AR. Data-driven Normative Reference of Pediatric Cranial Bone Development. Plast Reconstr Surg Glob Open. 2022 Aug 10;10(8):e4457. doi: 10.1097/GOX.0000000000004457. PMID: 35983543; PMCID: PMC9377678.)).