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Showing content from https://pubmed.ncbi.nlm.nih.gov/33206189/ below:

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

Comparative Study

. 2020 Nov 2;3(11):e2022199. doi: 10.1001/jamanetworkopen.2020.22199. Assessment of Facial Morphologic Features in Patients With Congenital Adrenal Hyperplasia Using Deep Learning

Affiliations

¹ Information Sciences Institute, University of Southern California, Los Angeles.
² Department of Electrical and Computer Engineering, University of Southern California, Los Angeles.
³ Division of Medical Genetics, Children's Hospital Los Angeles, Los Angeles, California.
⁴ Keck School of Medicine of the University of Southern California, Los Angeles.
⁵ Center for Endocrinology, Diabetes, and Metabolism, Children's Hospital Los Angeles, Los Angeles, California.
⁶ The Saban Research Institute at Children's Hospital Los Angeles, Los Angeles, California.

PMID: 33206189
PMCID: PMC7675110
DOI: 10.1001/jamanetworkopen.2020.22199

Item in Clipboard

Comparative Study

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

Wael AbdAlmageed et al. JAMA Netw Open. 2020.

. 2020 Nov 2;3(11):e2022199. doi: 10.1001/jamanetworkopen.2020.22199. Affiliations

¹ Information Sciences Institute, University of Southern California, Los Angeles.
² Department of Electrical and Computer Engineering, University of Southern California, Los Angeles.
³ Division of Medical Genetics, Children's Hospital Los Angeles, Los Angeles, California.
⁴ Keck School of Medicine of the University of Southern California, Los Angeles.
⁵ Center for Endocrinology, Diabetes, and Metabolism, Children's Hospital Los Angeles, Los Angeles, California.
⁶ The Saban Research Institute at Children's Hospital Los Angeles, Los Angeles, California.

PMID: 33206189
PMCID: PMC7675110
DOI: 10.1001/jamanetworkopen.2020.22199

Item in Clipboard

Abstract

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, <29 years) from face databases. With use of deep neural networks, a mean (SD) AUC of 92% (3%) was found for accurately predicting CAH over 6 folds. With use of classical machine learning and handcrafted facial features, mean (SD) AUCs of 86% (5%) in linear discriminant analysis and 83% (3%) in random forests were obtained for predicting CAH over 6 folds. There was a deviation of facial features between groups using deformation fields generated from facial landmark templates. Regionwise analysis and class activation maps (deep learning of regions) revealed that the nose and upper face were most contributory (mean [SD] AUC: 69% [17%] and 71% [13%], respectively).

Conclusions and relevance: The findings suggest that facial morphologic features in patients with CAH is distinct and that deep learning can discover subtle facial features to predict CAH. Longitudinal study of facial morphology as a phenotypic biomarker may help expand understanding of adverse lifespan outcomes for patients with CAH.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Geffner reported having a research contract with Novo Nordisk; receiving consultant fees from Adrenas, Daiichi Sankyo, Eton Pharmaceuticals, Ferring, Millendo Therapeutics, Neurocrine Bioscience, Novo Nordisk, Nutritional Growth Solutions, Pfizer, and QED; receiving royalties from McGraw-Hill and UpToDate; and serving on data safety monitoring boards for Ascendis, Millendo, and Tolmar. No other disclosures were reported.

Figures
Figure 1.. Congenital Adrenal Hyperplasia (CAH) Classification…

Figure 1.. Congenital Adrenal Hyperplasia (CAH) Classification Pipelines Using Handcrafted Features and Learned Representations

Illustration…

Figure 1.. Congenital Adrenal Hyperplasia (CAH) Classification Pipelines Using Handcrafted Features and Learned Representations

Illustration of our CAH classification pipelines, including various preprocessing steps of the input image and using both handcrafted features and learned representations. A, The input image was preprocessed by automatically detecting the face region in the image, detecting the locations of the 68 facial landmarks, and aligning and cropping the face region. B, A total of 27 handcrafted features were calculated using the detected landmarks. C, Classical machine learning classifiers, such as random forests, were used to predict the CAH score based on the handcrafted features. D, A deep neural network was used to extract learned representations from the preprocessed image and predict the CAH score without predefined features. CVL indicates convolutional layer; FCL, fully connected layer.

Figure 2.. Performance Analysis of Congenital Adrenal…

Figure 2.. Performance Analysis of Congenital Adrenal Hyperplasia (CAH) Scoring Using Machine Learning Techniques

Receiver…

Figure 2.. Performance Analysis of Congenital Adrenal Hyperplasia (CAH) Scoring Using Machine Learning Techniques

Receiver operating characteristic curves are shown for each method over 6 folds as well as the mean area under the curve (AUC). Shaded areas indicate SDs.

Figure 3.. Facial Landmark Templates of Averaged…

Figure 3.. Facial Landmark Templates of Averaged Facial Images in Patients With Congenital Adrenal Hyperplasia…
Figure 3.. Facial Landmark Templates of Averaged Facial Images in Patients With Congenital Adrenal Hyperplasia (CAH) and Control Individuals

Top, The computer-generated averaged amalgam faces of patients with CAH and controls by sex are shown. The second row visualizes the overlaid 68 facial landmarks of the control group (orange) and the group with CAH (blue). The bottom row visualizes the deformation field introduced by CAH, with the direction of the arrows moving from facial landmarks of controls to those of patients with CAH. This deformation field helps interpret the averaged facial images.

Figure 4.. Class Activation Maps and t-Distributed…

Figure 4.. Class Activation Maps and t-Distributed Stochastic Neighbor Embedding (t-SNE) Visualization

A, Red areas…

Figure 4.. Class Activation Maps and t-Distributed Stochastic Neighbor Embedding (t-SNE) Visualization

A, Red areas indicate the more contributory regions to the final predicted congenital adrenal hyperplasia (CAH) score. B, Visualization of the class activation maps for patients with CAH and controls.