Kids with Digeorge Syndrome Can Be Diagnosed With Facial Recognition Software

Kids with Digeorge Syndrome Can Be Diagnosed With Facial Recognition Software

Do you know about the DiGeorge syndrome? The DiGeorge syndrome, also called as 22q11.2 deletion syndrome and velocardiofacial syndrome, is a rare genetic disorder that can exhibit itself in different ways. The disorder causes several faults in the body such as heart defects, cleft palate, learning problems, and a distinctive facial appearance, it is often difficult for the healthcare providers to identify the disease owing to its several symptoms and rarity. However, it is known that the disease has an effect on the look of the faces of children.

Kids with Digeorge Syndrome Can Be Diagnosed With Facial Recognition Software

As there are only a few visual signs of the disease and also owing to the presence of multi-ethnic society, a general practitioner can simply fail to spot the snitch indications of DiGeorge, thus resulting in an incorrect diagnosis and improper follow-up care. Particularly, the kids belonging to non-European ethnicities are actually difficult to evaluate. The aim of the trial is to assist the healthcare professionals better diagnose and identify the 22q11.2 deletion syndrome, provide significant, early intervention, and offer better medical care.

Now a research team at the National Human Genome Research Institute has designed a computer vision algorithm, which can help in diagnosing kids for DiGeorge syndrome. The team firstly studied the photographs and clinical data of individuals with the disease. The look of an individual with the disorder varied extensively across the crowds. They then got access to several photos of kids suffering DiGeorge disorder from Asia, Africa, and Latin America. With the use of this information, the produced their algorithm, which they then applied to an analysis consisting of 150 photos of kids, involving European descent, without and with the condition. Remarkably, the software was able to make an appropriate prediction for 96.6% of the time. It is just a photo that is needed by the software and nothing else, which makes it capable of being easily ported to smartphones that can be utilized by the professionals.

The research team said they wish to further enhance the technique so that the healthcare professionals can one day take a smartphone photo of their patient, have it analyzed, and obtain a diagnosis.

Isn’t it a great step forward to diagnose this rare disease?


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