Understanding the relationship between skull shape, facial features and their genetic basis is essential in fields that work with craniofacial structures like clinical medicine and forensic science. By using AI techniques, we aim to generate new knowledge for the development of reconstructive craniofacial methods, useful for diagnosing and treating diseases, syndromes, and traumatic injuries affecting craniofacial structures.
RESEARCH QUESTION:
What are the key relationships between the genome, the skull structure, and the facial soft tissue?
OBJECTIVES:
To develop novel medical image segmentation algorithms to extract both skeletal and extra-cranial soft tissue structures from magnetic resonance images (MRI).
To identify patterns in skull shape and their relationship with soft tissue and facial features.
To model relationships between DNA markers and craniofacial structure to enable personalized facial reconstruction.
RATIONALE:
Although genetic associations with facial traits have been identified, reconstructive methods remain challenging due to limitations in precise genotype-phenotype mapping. By integrating precise imaging analysis with genomic data beyond single-nucleotide variation (e.g., structural variation, gene regulation networks), we aim to generate new knowledge about craniofacial development, advancing reconstructive methods for surgery and forensic applications.
To achieve this, AI-powered segmentation techniques will be developed to provide more precise, objective, and automated solutions for MRI analysis, overcoming limitations in tissue differentiation. Developing AI-powered segmentation techniques for MRI scans will enhance precision and automation across a broader range of clinical applications.
Altogether, this project will advance both basic science and technology, enabling new breakthroughs in craniofacial research.