information:extraction
Screening

In 2020, Wael AbdAlmageed from USC Information Sciences Institute and Mimi Kim from USC Keck School Of Medicine led a joint study in which they used deep learning technology to analyze the facial morphology and features of CAH patients compared to control. In this cross-sectional study of 102 patients with CAH and 144 control participants, deep learning methods achieved a mean area under the receiver operating characteristic curve of 92% for predicting CAH from facial images. Facial features distinguished patients with CAH from controls, and analyses of facial regions found that the nose and upper face were most contributory. The findings suggest that facial morphologic features, as analyzed by deep neural network techniques, can be used as a phenotypic biomarker to predict CAH.