# Detection of Pitt-Hopkins Syndrome Based on Morphological Facial Features
## 4 Discussion  
Recent studies show that facial analysis technologies improve the capabilities of expert clinicians in syndrome identification [11]. This work presents a novel facial analysis system for the diagnosis of PTHS. The ability to clinically diagnose PTHS is quite limited as evidenced by the numerous patients described in the literature diagnosed with a different clinical syndrome.  
The system proposed in this work exploits machine learning and computer vision technologies and learns facial representation from a dataset of faces. Previous studies have analyzed dysmorphic facial features of genetic syndromes [15], but only one ever focused on PTHS facial images using a dataset of five subjects [11]. Thus, it was not possible to achieve relevant results based on this study only. This is the first work which used a large dataset of children with molecular diagnosis of PTHS thanks to the collaboration with the Institute of Genomic Medicine, Gemelli Hospital Foundation, Rome, Italy. In this work, an automatic system for recognition of PTHS from facial images is proposed. The work focused on several items: picture acquisition and pre-processing, face detection, face registration and alignment, landmarks localization, features extraction, features selection, and classification. These methods were implemented and tested on a real and remarkable dataset of face images containing 61 subjects (32 PTHS patients and 29 healthy controls).  
In this work, a model which focuses on the problem of identifying the correct facial phenotype related to PTHS is presented. Methods such as HOG feature descriptors and cascade of regressors and their improvements were applied to face detection and alignment. Face detection and face alignment tasks were performed by the system with excellent results. All faces in the dataset were correctly detected and aligned.  
The landmark localization accuracy was dependent on image quality and pose variations. Non-frontal images can cause weak performance in the landmarks localization task. However, the method used and described in previous section works well for this task. The method was able to correctly localize facial landmark in PTHS and healthy controls. Starting from facial landmarks, features extraction and features selection tasks were based on geometric features. The geometric features are associated with specific dysmorphisms of the PTHS, such as the size of nose, and the size of the mouth. Geometrical features extracted in this work are suitable to depict the facial features of PTHS and healthy individuals, as indicated by the excellent accuracy rate reached in the last step of the system, the classification task. This is the first study to the author's knowledge to investigate the efficiency of a system based on computer vision and machine learning algorithms in order to recognize PTHS subjects from face images. The k-nearest neighbor classifier reached the accuracy of 91.8% and an F-score of 0.918 on the dataset of 61 images. On the same dataset, a pediatrician obtained an accuracy of 74% and F-score of 0.722. The k-NN classifier was also validated with 31 additional photographs, obtaining an accuracy of 94% and an F-score of 0.94. The previous data on a small dataset of five patients reached an accuracy of 80% [11]. A statistical independent evaluation of each geometric feature revealed that RMouth and NoseAngle are the features most significantly correlated with PTHS phenotype.  
This work demonstrated the possibility to describe phenotypes in a standardized manner, opening the door to the emerging field of precision medicine, as well as to the identification of new genetic syndromes by matching undiagnosed patients sharing a similar phenotype. Patient matching with such artificial intelligence technology will enhance the way that genetic syndromes and other genetically caused diseases are studied and explored.