Diagnosing rare and difficult-to-treat genetic conditions using apps on mobile phones sounds like pure science fiction.But Gurovich, chief scientist at FNDA, and colleagues in the US, have turned the concept into reality.
Recently, the world's top academic journal Nature-Medical published an article using facial image analysis to detect hereditary diseases. Researchers at FDNA trained a computer deep learning algorithm by using a facial image dataset of 17,000 patients. This algorithm can help diagnose hereditary diseases with a accuracy rate of 91%, which exceeds that of many experts and clinicians.
which can diagnose genetic syndromes by taking facial photographs; timely diagnosis of genetic syndromes can improve prognosis.However, there are more than 8,000 known genetic syndromes. The number of genetic syndromes that may occur in patients is numerous and rare. It takes a long time to make a correct diagnosis and is expensive.In addition, the diagnosis of non-classical manifestations or supertypical syndromes is limited by prior experience of clinical experts, which makes computer systems increasingly important as a reference.
The study states that 8% of the population may have a genetic syndrome and that many have identifiable facial features.For example, Angelman syndrome, a disease affecting the nervous system, has many typical facial features, such as wide spacing of teeth, strabismus, protruding tongue, etc.Therefore, it is possible to identify genetic syndromes based on facial features.
Early computer-aided syndrome recognition techniques have shown promise in helping clinicians diagnose by analyzing patients' facial images.But the training data set used by studies on this possibility is small and only identifies a small number of syndromes.
R & D staff at FDNA, based in Boston, Gurovich and colleagues developed an app — Face2Gen.This app is based on an AI technology, DeepGestaltTM (New Facial Image Analysis Framework), which uses computer vision and deep learning algorithms to successfully identify the facial phenotypes of hundreds of genetic diseases.Gurovich and his team trained DeepGestalt by using facial images from 17,000 patients in a database that diagnosed more than 200 different genetic syndromes.
2, accuracy up to 91%
In the new research paper, the researchers explained in depth how this technique works.First the facial image is input, face detection is performed using a cascading method based on DCNN (depth convolution neural network), the input image is cropped into multiple facial regions, and each region is fed into DCNN to obtain a softmax function (flexible maximum transfer function) indicating its correspondence with each syndrome in the model.The output functions of DCNN in all regions are then summed and classified to obtain a final ranked list of genetic syndromes.Histograms on the right indicate the genetic syndromes output by DeepGestalt, sorted by the summed similarity scores.
The team found that this AI technique significantly outperformed clinicians in two different sets of tests for determining genetic syndromes in 502 selected facial images.In each test, the list of potential syndromes generated by this AI has a 91% probability of identifying the correct genetic syndrome in the top 10 recommendations.
Another test aims to identify different genetic subtypes of Noonan syndrome with a unique set of features and health problems such as heart defects.In this test, the AI technique deep learning algorithm had a success rate of 64%, and in a previous study, clinicians observing images of patients with Noonan syndrome were able to identify only 20% of casesHowever, the researchers also said that AI technology can only be used as an aid, and the results need to be finalized by doctors.
3, beware of abuse risk
Karen Gripp, co-author of the paper, said that the importance of this paper lies in the detailed description of how to train the algorithm and how it works.Although there are other similar systems, no system has so many cases and diseases to analyze.This paper creates standards for comparison with other systems and provides a reference for using this tool for other studies.
Gripp hopes that the next step is to use this technique to analyze the lateral view of the face, which can also be useful information at the time of diagnosis.She also wants more data on ethnic backgrounds, since the vast majority of uploaded faces are European.However, she points out that the technique performs well in different races.Currently FNDA is developing embedded solutions that use the technology and can be licensed to other healthcare and technology organizations to use the technology on their own platforms.
In addition, the researchers also acknowledged that the technology has certain risks.Because it is too easy to shoot the face, the technology could be misused by employers or insurance companies.They say proper regulation of the distribution and use of tools such as DeepGestalt is essential.
Original: Identifying facial phenotypes of genetic disorders using deep learning
Author: Yaron Gurovich, Yair Hanani, Karen W.Gripp, et al