Computer Vision Predicts Congenital Adrenal Hyperplasia

Alex Baker and Amy Blumenthal | November 19, 2020

New Tool Offers Means to Phenotype Patients

Image credit: Istock

Image credit: Istock

Researchers at the VISTA Center (Vision, Image, Speech and Text Analytics) at the USC Viterbi Information Sciences Institute (ISI) along with scholars at the Keck School of Medicine of USC and Children’s Hospital Los Angeles (CHLA) have discovered strong correlations between facial morphology and congenital adrenal hyperplasia (CAH), a life-threatening genetic condition of the adrenal glands and one of the most common forms of adrenal insufficiency in children. The findings, which could have implications for phenotyping and treating patients with CAH, appeared today in the Journal of the American Medical Association Network Open.

Dr. Mimi Kim, associate professor of clinical pediatrics at the Keck School of Medicine of USC, suspected that facial features were affected by this condition. Based on her work, computer scientists used artificial intelligence (AI) to generate facial models from iPad photos taken by doctors in clinic and then applied AI to analyze these images to distinguish differences between the facial structure of youth affected with CAH versus others without CAH.

The paper’s lead author, Wael Abd-Almageed, who is an associate research professor in the USC Viterbi Department of Electrical and Computer Engineering and a Research Team Leader at the USC ISI, says this breakthrough “can open up the door to better clinical outcomes and improving quality of life for patients.” He says one can imagine that doctors in the future can use this tool to assess disease progression.

Until now, the link between CAH and facial morphology was unexplored, since the effects of CAH on facial structure are relatively subtle compared to other genetic conditions such as Down syndrome Because of the subtlety of the differences in facial features and the difficulty involved in taking precise facial measurements by hand, the team used artificial intelligence to assist in the study.

Combined faces of male study participants in each group. Left is control, right is CAH. (Photo/Courtesy of CHLA)

Computer generated image of potential patients

According to Dr. Mimi Kim, a lead on the project and the co-director of the CAH Comprehensive Care Clinic at CHLA, this discovery unlocks new areas of research that can help us better understand the disease. Kim began to suspect common variations in facial features between patients with CAH at the center, and to confirm her suspicions, partnered with the USC ISI team to investigate. The discovery will not be used to identify or diagnose severe forms of CAH, which is screened for nationwide in all newborns. Rather, it opens the door for new clinical applications.

“Our first goal of this project was to find out whether differences in facial morphology can be identified in patients with CAH compared to unaffected individuals,” said Hengameh Mirzaalian, a machine learning and computer vision scientist at ISI, and member of the CAH research team. The team used machine learning to train a computer to recognize individuals with CAH by analyzing an image of their face. This was accomplished by first showing the computer labeled images of faces of individuals both with and without CAH.

Ultimately, understanding the distinct facial features that accompany CAH is an important step towards learning more about other issues associated with the condition, such as hormonal imbalances that begin in early pregnancy, and improving treatment,” said Mirzaalian.

Published on November 19th, 2020

Last updated on May 16th, 2024

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