Researchers Use Facial Recognition Software to Diagnose a Rare, Genetic Disease

Researchers with the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health, and their collaborators, have successfully used facial recognition software to diagnose a rare, genetic disease in Africans, Asians and Latin Americans.


Facial recognition systems are commonly used for security purposes. They rely on faceprints can quickly and accurately identify target individuals when the conditions are favorable. Nowadays they are increasingly being used in a variety of other applications. Recently, scientists use facial recognition software to diagnose a rare, genetic disease that mainly affects children.

This technique can diagnose DiGeorge syndrome or velocardiofacial syndrome. It exactly performs like Softwares that used in Airport or Facebook. It can efficiently diagnose the disease, 22q11.2 deletion syndrome. DiGeorge syndrome also called as 22q11.2 deletion syndrome is a disorder caused by the deletion of a small piece of chromosome 22. It causes multiple defects throughout the body, including cleft palate, heart defects, a characteristic facial appearance and learning problems, healthcare providers often cannot pinpoint the disease, especially in diverse populations.

Marius George Linguraru, an investigator at the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Health System in Washington, developed the digital facial analysis technology used in the study.

Paul Kruszka from National Human Genome Research Institute (NHGRI) said, “Human malformation syndromes appear different in different parts of the world. Even experienced clinicians have difficulty diagnosing genetic syndromes in non-European populations.”

Scientists actually wanted help healthcare providers better recognize and diagnose the DiGeorge syndrome. The facial recognition technique delivers critical, early interventions and offer better medical care.

Scientists primarily studied the clinical information and photographs of participants with the disease. The appearance of someone with the disease varied widely across the groups.

They then used facial recognition and compared a group of 156 affected persons from Africa, Asia, and Latin America with non-affected people. As scientists noted, they made almost 96.6 percent correct diagnosis based on 126 different facial features.

Scientists said, “We hope to further develop the technology so that healthcare providers can one day take a cell phone picture of their patient, have it analyzed and receive a diagnosis.”

- Advertisement -

Latest Updates