Comparing facial feature extraction methods in the diagnosis of rare genetic syndromes

Background and Objective Since several genetic disorders exhibit facial characteristics, facial recognition techniques can help clinicians in diagnosing patients. However, currently, there are no open-source models that are feasible for use in clinical practice, which makes clinical application of these methods dependent on proprietary software. Methods In this study, we therefore set out to compare three facial feature extraction methods when classifying 524 individuals with 18 different genetic disorders: two techniques based on convolutional neural networks (VGGFace2, OpenFace) and one method based on facial distances, calculated after detecting 468 landmarks. For every individual, all three methods are used to generate a feature vector of a facial image. These feature vectors are used as input to a Bayesian softmax classifier, to see which feature extraction method would generate the best results. Results Of the considered algorithms, VGGFace2 results in the best performance, as shown by its accuracy of 0.78 and significantly lowest loss. We inspect the features learned by VGGFace2 by generating activation maps and using Local Interpretable Model-agnostic Explanations, and confirm that the resulting predictors are interpretable and meaningful. Conclusions All in all, the classifier using the features extracted by VGGFace2 shows not only superior classification performance, but detects faces in almost all images that are processed, in seconds. By not retraining VGGFace2, but instead using the feature vector of the network with its pretrained weights, we avoid overfitting the model. We confirm that it is possible to classify individuals with a rare genetic disorder (thus by definition using a small dataset) using artificial intelligence and open-source all of the models used in this study, being the first study to open-source deep learning algorithms to be used to assess facial features in clinical genetics. Concise abstract Since several genetic disorders exhibit facial characteristics, facial recognition techniques can help clinicians in diagnosing patients. However, there are no open-source models available that are feasible for use in clinical practice, which makes clinical application of these methods dependent on proprietary software. This hinders not only use in clinic, but academic research and innovation as well. In this study, we therefore set out to compare three facial feature extraction methods for classifying 524 individuals with 18 different genetic disorders: two techniques based on convolutional neural networks and one method based on facial distances. For every individual, all three methods are used to generate a feature vector of a facial image, which is then used as input to a Bayesian softmax classifier, to compare classification performance. Of the considered algorithms, VGGFace2 results in the best performance, as shown by its accuracy of 0.78 and significantly lowest loss. We inspect the learned features and show that the resulting predictors are interpretable and meaningful. We confirm that it is possible to classify individuals with a rare genetic disorder (thus by definition using a small dataset) using artificial intelligence and open-source all of the models used in this study. This is the first study to open-source deep learning algorithms to assess facial features in clinical genetics.