Dysmorphic feature

One of the key challenges in identifying and describing dysmorphic features is the use and understanding of specific terms between different individuals.

[4] Most open source projects that perform phenotype-driven disease or gene prioritization work with the terminology of the Human Phenotype Ontology.

Publicly accessible databases that labs use to deposit their diagnostic findings, such as ClinVar, can be used to build knowledge graphs to explore the clinical feature space.

In the recent years advances in computer vision have also resulted in several deep learning approaches that assist geneticists in the study of the facial gestalt.

[6][7][8] Training and test data for clinicians and computer scientists in order to compare the performance of new AIs can be obtained from GestaltMatcher.