Quantifying dysmorphologies of the neurocranium using artificial neural networks.

ABSTRACT:

Background

Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for evaluating cranial morphology to enhance patient treatment. Current subjective assessments often lead to inconsistent outcomes. This study introduces a novel, quantitative approach to classify craniosynostosis and measure its severity.

Methods

An artificial neural network was trained to classify normocephalic, trigonocephalic, and scaphocephalic head shapes based on a publicly available dataset of synthetic 3D head models. Each 3D model was converted into a low-dimensional shape representation based on the distribution of normal vectors, which served as the input for the neural network, ensuring complete patient anonymity and invari

SUBMITTER: Abdel-Alim T 

PROVIDER: S-EPMC11547242 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature