Work

Advances in predictive modeling of cranial growth in craniosynostosis: A systematic review and meta-analysis

Year: 2026

Type: review

Study designs: Systematic Review, Meta-Analysis

Abstract: Background Craniosynostosis, characterized by premature fusion of cranial sutures, disrupts normal skull growth and may lead to neurological and functional complications. Despite advances in surgery, planning remains largely experience-based due to limited patient-specific predictive tools. Methods A PRISMA-compliant systematic review was conducted across PubMed/MEDLINE, Scopus, and Web of Science (January 2020 to November 2025). Eligible studies enrolled pediatric craniosynostosis patients using predictive or morphometric modeling. Risk of bias was assessed using PROBAST, QUIPS, and CASP. A random-effects meta-analysis was performed where applicable. Results Twenty-nine studies were included, spanning FEA ( n = 14), statistical shape and regression models ( n = 9), and machine learning approaches ( n = 3). Meta-analysis of four studies reporting cephalic index showed a non-significant pooled effect (MD = −1.23; 95% CI −3.35 to 0.89; p = 0.26; I 2 = 57%), with moderate heterogeneity limiting conclusions. Meta-analysis of two studies reporting intracranial volume showed a significant reduction (SMD = −1.81; 95% CI −3.32 to −0.31; p = 0.02; I 2 = 77%), though the high heterogeneity warrants cautious interpretation given the very limited number of contributing studies. Most studies (69%) were rated as moderate risk of bias, with common limitations including small single-center samples, retrospective designs, and limited external validation. Conclusions FEA, statistical shape models, and ML approaches each offer complementary strengths for surgical planning in craniosynostosis. Future research should prioritize multicenter validation, radiation-free imaging, and standardized reporting to enable patient-specific clinical deployment.

Source: Journal of Cranio-Maxillofacial Surgery

DOI: 10.1016/j.jcms.2026.109869

Authors: Ashwag Alsedran, Norli Anida Abdullah, Nur Anisah Mohamed, Firdaus Hariri

Institution: University of Malaya

Language: English

Topic: Craniofacial Disorders and Treatments

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,784