Assessment of Facial Morphologic Features in Patients With Congenital Adrenal Hyperplasia Using Deep Learning

JAMA Network Open
Wael AbdAlmageedMimi S Kim

Abstract

Congenital adrenal hyperplasia (CAH) is the most common primary adrenal insufficiency in children, involving excess androgens secondary to disrupted steroidogenesis as early as the seventh gestational week of life. Although structural brain abnormalities are seen in CAH, little is known about facial morphology. To investigate differences in facial morphologic features between patients with CAH and control individuals with use of machine learning. This cross-sectional study was performed at a pediatric tertiary center in Southern California, from November 2017 to December 2019. Patients younger than 30 years with a biochemical diagnosis of classical CAH due to 21-hydroxylase deficiency and otherwise healthy controls were recruited from the clinic, and face images were acquired. Additional controls were selected from public face image data sets. The main outcome was prediction of CAH, as performed by machine learning (linear discriminant analysis, random forests, deep neural networks). Handcrafted features and learned representations were studied for CAH score prediction, and deformation analysis of facial landmarks and regionwise analyses were performed. A 6-fold cross-validation strategy was used to avoid overfitting and bias. ...Continue Reading

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Methods Mentioned

BETA
feature extraction

Software Mentioned

NumPy
Scipy
Python

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