Assessment of Facial Morphologic Features in Patients With Congenital Adrenal Hyperplasia Using Deep Learning
| Autor: | Mimi S. Kim, Veeraya K Tanawattanacharoen, Hengameh Mirzaalian, Xiao Guo, Linda M. Randolph, Heather M. Ross, Wael AbdAlmageed, Mitchell E. Geffner |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Pediatrics medicine.medical_specialty Longitudinal study Adolescent Biochemical diagnosis California Young Adult Text mining Deep Learning Image Processing Computer-Assisted Medicine Humans Congenital adrenal hyperplasia In patient Longitudinal Studies Child Nose Original Investigation Adrenal Hyperplasia Congenital business.industry Deep learning Research Age Factors General Medicine medicine.disease Linear discriminant analysis Online Only Diabetes and Endocrinology medicine.anatomical_structure Cross-Sectional Studies Child Preschool Face Female Artificial intelligence business |
| Zdroj: | JAMA Network Open |
| ISSN: | 2574-3805 |
| Popis: | This cross-sectional study evaluates the use of machine learning for prediction of congenital adrenal hyperplasia based on distinct facial morphologic features. Key Points Question Do patients with congenital adrenal hyperplasia (CAH) have distinct facial morphologic features that are distinguishable by deep learning? Findings 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. Meaning The findings suggest that facial morphologic features, as analyzed by deep neural network techniques, can be used as a phenotypic biomarker to predict CAH. Importance 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. Objective To investigate differences in facial morphologic features between patients with CAH and control individuals with use of machine learning. Design, Setting, and Participants 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. Main Outcomes and Measures 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. Results The study included 102 patients with CAH (62 [60.8%] female; mean [SD] age, 11.6 [7.1] years) and 59 controls (30 [50.8%] female; mean [SD] age, 9.0 [5.2] years) from the clinic and 85 controls (48 [60%] female; age |
| Databáze: | OpenAIRE |
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