Artificial Intelligence Helps Detect Rare Diseases

For each separate case, frontal photos, clinical features, and the disease-causing variants were submitted. The results showed that computer-assisted analysis of frontal photos improved the top 1% accuracy rate by more than 20–89%, and the top 10% accuracy rate by more than 5–99% for the disease-causing gene. The study was published on June 5, 2019, in Nature Genetics in Medicine.

“In combination with facial analysis, it is possible to filter out the decisive genetic factors and prioritize genes. Merging data in the neuronal network reduces data analysis time and leads to a higher rate of diagnosis,” said senior author Professor Peter Krawitz, MD, PhD, director of the Institute for Genomic Statistics and Bioinformatics at the University of Bonn. “This is of great scientific interest to us and also enables us to find a cause in some unsolved cases.”

“PEDIA is a unique example of next-generation phenotyping technologies,” said Dekel Gelbman, CEO of FDNA. “Integrating an advanced AI and facial analysis framework such as DeepGestalt into the variant analysis workflow will result in a new paradigm for superior genetic testing.”

Many rare diseases cause characteristic abnormal facial features in those affected, such as Kabuki syndrome, which is reminiscent of the make-up of a traditional Japanese form of theatre. The eyebrows are arched, the eye-distance is wide and the spaces between the eyelids are long. Another example is mucopolysaccharidosis, which leads to bone deformation, stunted growth, and learning difficulties. Such phenotype information has so far only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.


Related Links:
University of Bonn
GeneTalk
Charité University Medicine
FDNA