Rare Daily Staff
Greenwood Genetic Center said it has entered into a collaboration with FDNA, the developer of the Face2Gene suite of applications that uses artificial intelligence and facial recognition to help diagnose patient with rare, genetic diseases.
The collaboration will use FDNA’s technology to analyze de-identified patient data from Greenwood Genetic Center with the goal of aiding in new discoveries of genetically-based rare diseases.
The diagnostic odyssey for rare disease patients remains long and difficult. It takes on average seven years and visits to seven different specialists to arrive at a correct diagnosis. Even though the cost of genetic testing is rapidly falling and becoming more available, the lack of patient data to identify genetic variations that are clinically meaningful remains a challenge. Only one in four patients that undergo molecular testing will end up with a diagnosis, according to FDNA.
FDNA is working to build a large database of genetic data linked to phenotypic data. Through its facial recognition software, FDNA analyzes the physical appearance of patients that may point to underlying genetic conditions.
“Because of the complexity of the human genome and the sheer number of rare syndromes, geneticists have been, up to now, at an extreme disadvantage in coming to a diagnosis,” said Dekel Gelbman, CEO of FDNA. “Combining AI with big data is giving medical professionals the help they need to provide hope to these patients.”
Under the collaboration, FDNA is expected to analyze nearly 80,000 cases from the center. This will be used to improve FDNA’s database of known diagnoses and provide potential insight to patients who are without a diagnosis. Greenwood Genetic Center will analyze thousands of patients who are without a diagnosis in the hopes of producing answers for them.
“The GGC clinical team has already received the first insights from FDNA,” said Hannah Warren, Clinical Genetic Counselor at Greenwood Genetic Center. “There are dozens of high priority, undiagnosed cases that have been flagged by Face2Gene due to their statistically significant facial analysis insights. These new insights may help find a diagnosis for patients who have been searching for answers for much too long.”
September 6, 2017