Face recognition systems can diagnose genetic disorders that are complex for doctors

Recent years have seen an exponential rise in the use of facial recognition systems. From airports to railways and border control to cities' streets, facial recognition is being deployed everywhere to instantly identify suspicious people to prevent crimes. Rapid technological advances have led to a wide proliferation of this technology. This deep learning-based technique is now finding its way into effective care delivery, assisting healthcare organizations to diagnose rare genetic disorders that are not readily apparent to doctors. As most of all medical data are stored in the form of images, face recognition technology visualizes those images to excerpt data for accurate treatment.

In 2019, a study was published in the journal Nature Medicine, where U.S. company FDNA promulgated new tests of their software, DeepGestalt, the technology powering Face2Gene. According to the study, the software outpaced clinicians in three initial experiments, two of which intending to distinguish subjects with a target syndrome from other syndromes, and one of separating different genetic subtypes in Noonan syndrome. On the final experiment reflecting a real clinical setting problem, DeepGestalt achieved 91% top-10 accuracy in spotting the correct syndrome on 502 different images. To perform this, FDNA trained their algorithms by assessing a dataset of faces. They trained their model on a dataset of over 17,000 images representing more than 200 different syndromes using Face2Gene, a community-driven phenotyping platform.

Let's look at some face recognition trends.

Already, facial recognition has become a crucial evolving part of the biometrics market and digital transformation efforts worldwide. Soaring investments in this tech are continuously making it mature software across diverse areas and giving rise to its use cases. Be it making advertising more targeted and smarter or unlocking phones and dodging crimes, the use cases of facial recognition are much wider.

In the healthcare space, medical professionals have been using a face recognition system as an aid, although it is not intended to deliver definitive diagnoses. It is able to track patients and others within a facility without the need for a physical tracking device. The system could also identify whether a specific person should have access to certain areas or other restricted locations.

When combined with AI, facial monitoring and eye-tracking can recognize and diagnose certain diseases that are complex to clinicians. These technologies leverage modern computers to evaluate, sort, and find patterns across voluminous amounts of data and serve as an extension to a doctor's experience and knowledge allowing faster and more precise diagnoses. For instance, RightEye, a commercialized eye-tracking solution provider, develops an automated autism test. The model's eye-tracking test recently projected autism spectrum disorder 86% of the time in over 400 toddlers.

In today's current scenario, as the onslaught of COVID-19 has taken the world by storm, AI and face recognition technologies emerge as the frontline solution. Using face recognition, administrations could able to track down people affected by the disease. The technology is currently being used to manage the pandemic by scanning public spaces for people potentially infected to maintain social distancing.