41. AI Radiographic Severity Assessment and Age at Surgery Associated with Neurocognitive Outcomes in Metopic Craniosynostosis

PURPOSE: Radiographic severity of metopic synostosis has been suggested as a possible predictor of neurocognitive outcomes. Recently, artificial intelligence (AI) has been used to quantify severity. Age at surgery has been shown to predict long-term neurocognition in sagittal synostosis but has not been adequately explored in metopic synostosis. METHODS: Children ages 6-18 years old with surgically-corrected metopic synostosis underwent testing of intelligence quotient, academic achievement, and visuomotor integration (VMI). Endocranial bifrontal angle (EBA), adjusted EBA, frontal angle, and AI-derived metopic severity (MSS) and cranial morphology deviation (CMD) scores were determined. Scans were categorized as moderate or severe (EBA<124). Multivariate linear regressions were used to assess the association of age at surgery and severity with neurocognitive outcomes. RESULTS: 41 patients with average age at surgery 8.6±6.5 months and average age at testing 10.8±3.4 years old were included. Patients in the severe group received surgery at a younger age (6.4±2.6 vs 10.3±8.1 months, p=0.03). Multivariate linear regression models identified associations between reading comprehension and MSS (p=0.04) and CMD (p=0.018) as well as for reading composite and age at surgery (p=0.04), MSS (p=0.02) and CMD (p=0.01). Age at surgery was associated with VMI in all 6 models controlling for each severity measure (p<0.05). CONCLUSION: Pre-operative radiographic severity of metopic synostosis and age at surgery were associated with neurocognitive outcomes at school age. Lower AI-derived MSS and CMD scores had better reading outcomes, while younger age at surgery had better visuomotor integration. Prompt surgical correction of metopic synostosis may be important to reduce neurodevelopmental differences.