AI Diagnoses Genetic Disorder from Facial Photos with High Accuracy

A team from Yale School of Medicine has reported a breakthrough in diagnosing Marfan syndrome using artificial intelligence (AI). The AI model demonstrated remarkable accuracy in identifying the genetic condition from simple facial photographs.
Marfan syndrome is a rare genetic disorder affecting around 1 in 3,000 people, characterized by tall stature, long faces, and issues with connective tissues, spine, and joints. Early diagnosis is crucial due to the risk of life-threatening aortic dissection, which often requires urgent surgery.
In a recent study published in *Heliyon*, researchers used a Convolutional Neural Network to analyze 672 facial photographs of individuals with and without Marfan syndrome. The AI model, trained on 80% of the images, achieved a 98.5% accuracy rate in distinguishing between Marfan and non-Marfan faces.
The team plans to make this diagnostic tool available online, aiming to allow individuals to self-test and enhance early diagnosis. “We are excited to expand this research and provide a tool that could significantly impact early detection and treatment,” said John Elefteriades, MD, professor of surgery and senior author of the study.
Nancy J. Brown, MD, dean of Yale School of Medicine, highlighted the innovative use of AI in recognizing and diagnosing rare diseases, underscoring its potential to improve early intervention.

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