According to a recent study, a new artificial intelligence technology can accurately identify rare genetic disorders using a photograph of a patient’s face.
Named DeepGestalt, the AI technology outperformed clinicians in identifying a range of syndromes in three trials and could add value in personalised care, CNN reported.
The study was published in the journal Nature Medicine.
According to the study, eight per cent of the population has disease with key genetic components and many may have recognisable facial features.
The study further adds that the technology could identify, for example, Angelman syndrome, a disorder affecting the nervous system with characteristic features such as a wide mouth with widely spaced teeth etc.
Speaking about it, Yaron Gurovich, the chief technology officer at FDNA and lead researcher of the study said, “It demonstrates how one can successfully apply state of the art algorithms, such as deep learning, to a challenging field where the available data is small, unbalanced in terms of available patients per condition, and where the need to support a large amount of conditions is great.”
The study saw researchers training DeepGestalt, a deep learning algorithm, by using 17,000 facial images of patients from a database of patients diagnosed with over 200 distinct genetic syndromes.
The team found that the AI technology outperformed clinicians in two separate tests to identify a target syndrome among 502 chosen images.
In each test, the AI proposed a list of potential syndromes and identified the correct syndrome in its top 10 suggestions 91 per cent of the time.
Another test looked into identifying different genetic subtypes in Noonan syndrome, which carries a range of distinctive features and health problems, such as heart defects. Here, the algorithm had a success rate of 64 per cent.
According to researchers, the technology works by applying the deep learning algorithm to the facial characteristics of the image provided, then producing a list of possible syndromes.
However, the study added that it does not explain which facial features led to its prediction. To help the researchers better understand, the technology produces a heat map visualisation looking at what regions of the face contributed to the classification of diseases, explained Gurovich.