# Detection of Pitt-Hopkins Syndrome Based on Morphological Facial Features
###### Abstract  
This work describes a non-invasive, automated software framework to discriminate between individuals with a genetic disorder, Pitt-Hopkins syndrome (PTHS), and healthy individuals through the identification of morphological facial features. The input data consist of frontal facial photographs in which faces are located using histograms of oriented gradients feature descriptors. Pre-processing steps include color normalization and enhancement, scaling down, rotation, and cropping of pictures to produce a series of images of faces with consistent dimensions. Sixty-eight facial landmarks are automatically located on each face through a cascade of regression functions learnt via gradient boosting to estimate the shape from an initial approximation. The intensities of a sparse set of pixels indexed relative to this initial estimate are used to determine the landmarks. A set of carefully selected geometric features, for example, the relative width of the mouth or angle of the nose, is extracted from the landmarks. The features are used to investigate the statistical differences between the two populations of PTHS and healthy controls. The methodology was tested on 71 individuals with PTHS and 55 healthy controls. The software was able to classify individuals with an accuracy rate of 91%, while pediatricians achieved a recognition rate of 74%. Two geometric features related to the nose and mouth showed significant statistical difference between the two populations. Pitt-Hopkins syndrome; morphological face analysis; facial landmarks +
Footnote †: journal: Medical Sciences  
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is arched with flaring nostrils, an evident curved shape of the upper lip (also known as Cupid's bow), and wide spaces between teeth [2; 5; 6]. Diagnosis of PTHS typically starts on a clinical consultation in which facial signs and other factors, such as absent speech, EEG abnormalities or breathing abnormalities, are evaluated [7; 8; 9]. In some cases, the diagnosis is confirmed by genetic testing, looking for either deletions of the chromosome region in which the protein transcription factor 4 (TCF4) is located or the identification of a heterozygous variant of TCF4 [10]. Diagnosis of syndromes such as PTHS with genetic tests can be expensive, time consuming, and not available to some communities [11], and thus, not always performed when diagnosing. In a comparative study, only around 62% of diagnoses included genetic testing [7]. Thus, the identification of specific, quantifiable facial metrics extracted from photographs, which can be related to genotype and phenotype would be attractive as a pre-diagnosis step for individuals with PTHS, especially in those cases where the facial dysmorphism is not quite evident. The recognition of a genetic syndrome based on a photograph is rather similar to the problem of classic facial recognition [12]. However, genetic syndrome detection is limited by the amount of data available and the imbalance against controls [13], the difficulty in differentiating between subtle facial patterns, gender, and ethnic differences [14], among other factors [15]. Facial characteristics have been correlated with other conditions, such as diabetes mellitus based on texture [16], block colors [17] and chromatic features [18]. Furthermore, the limited amount of data restricts the possibility of developing solutions based on deep learning approaches [19; 20; 15; 21] as these typically require very large amounts of training labelled data. In addition, besides the requirement of training data and significant computational resources, deep learning and in general Artificial Intelligence (AT) have sometimes been criticized for being a \"black box\" [22; 23] which requires significant work to make them interpretable and/or explainable. The research areas of Explainable AI [24; 25; 26] and Interpretable AI [27; 28] are fast growing as a way to provide tractable explanations to humans.