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Purpose: Classical Congenital Adrenal Hyperplasia (CAH) due to 21-hydroxylase deficiency affects 1:15,000 newborns and involves adrenal insufficiency and androgen excess. These hormone abnormalities are evident as early as 7 weeks’ gestation and persist throughout pregnancy. Structural brain abnormalities are also known to occur in CAH, with abnormalities of brain and facial structure occurring together in conditions such as fetal alcohol spectrum disorder and holoprosencephaly. As well, sex differences in facial morphology are well described in healthy individuals. Thus, we aimed to study facial features using artificial intelligence in CAH youth. Methods: We studied frontal images of the face in 57 youth with severe salt-wasting CAH (60% female; 9.4±5.5y), and 38 controls (47% female, 9.7±5.1y), acquired with an iPad v12.1. We included 32 additional controls (43% female; 4-19y) from a publicly available face image dataset (1). Applying deep learning techniques, we converted 2-D facial photos to mathematical descriptors in order to differentiate features between groups. For a given test image, our pipeline output was a predicted “CAH score” between [0,1]. Due to our small dataset, we employed K-fold cross validation to train and test our deep neural network. At each of the K-9 folds, 88% of data (468 control and 531 CAH images) were used to train the network, with the remaining data (55 control and 63 CAH images) used to test the trained network. Test results were validated in terms of area under the curve (AUC) of receiver operating characteristic curves (generated from predicted CAH scores of test subjects), to analyze true and false positive rates. Our pipeline automatically detected face-bounding boxes and 68 facial landmarks (dlib toolkit) which were then used to compute 27 Euclidean (linear) facial features (2,3). We performed between group analyses of features with t-tests. Results: The averaged AUC of nine folds was 0.83±0.14, representing strong predictive power as a proxy to correlating facial dysmorphology with CAH. Predicted CAH scores were different between control (0.24±0.33) and CAH (0.69±0.37; p&amp;lt;0.0001) youth. Thirteen of 27 facial features were different between controls and CAH (p&amp;lt;0.05 for all) including 3 of 6 features related to sexual dimorphism. We also produced heat (i.e., saliency) maps showing the effect of CAH on facial features, and 2D t-SNE plot visualization of features showing well-defined separation between CAH and control group clusters. Conclusions: Utilizing deep learning, we have shown that CAH youth have facial features that can reliably distinguish them from controls. Further study is merited in regard to the etiology of affected facial morphology in CAH, and associations with disease severity, and/or brain and behavior abnormalities. (1) Masi I et al. Int J Comput Vis 2019. (2) Whitehouse AJ et al. Proc Biol Sci 2015. (3) Lefevre CE et al. Evol Hum Behav 2013

SAT-087 Identifying Distinct Facial Dysmorphology in Youth with Congenital Adrenal Hyperplasia Using Deep Learning Techniques