0
0
Yale School of Medicine researchers develop a pioneering AI tool with 98.5% accuracy, promising earlier detection and better management of the genetic disorder.
In a groundbreaking study, a team from Yale School of Medicine has demonstrated that an artificial intelligence (AI) model can reliably diagnose Marfan syndrome—a rare genetic disorder—by analyzing simple facial photographs. This innovative approach has the potential to revolutionize the diagnosis and management of the condition, which affects approximately 1 in 3,000 individuals worldwide.
Marfan syndrome is a disorder that impacts the body’s connective tissues, leading to a variety of physical manifestations. “Patients living with Marfan syndrome are usually very tall and thin,” explained Dr. John Elefteriades, Professor of Surgery at Yale School of Medicine and the senior author of the study. “They have long faces and are prone to spine and joint issues. However, many are not diagnosed.”
One of the most dangerous complications of Marfan syndrome is aortic dissection—a condition where the aorta, the main artery in the body, splits suddenly after becoming enlarged. “It is often lethal, and when the patient survives, urgent surgery is needed,” Elefteriades said. “Being able to identify individuals from a photograph with AI will enhance diagnosis and enable protective therapies.”
The pilot study, recently published in Heliyon, involved the collection of 672 facial photographs of people with and without Marfan syndrome. Using these images, the researchers trained a Convolutional Neural Network (CNN) on 80% of the photographs, then tested the model’s ability to distinguish between Marfan and non-Marfan faces on the remaining 20%. The AI model achieved a remarkable 98.5% accuracy in identifying Marfan syndrome from the photographs.
The success of this pilot study has paved the way for future developments. “We are planning to extend this work beyond this initial pilot project,” said Elefteriades. “We anticipate that many individuals may self-test once we put the test online.”
This AI-based diagnostic tool holds great promise for improving the early detection of Marfan syndrome, particularly in cases where physical symptoms may not be immediately recognized by clinicians. “Yale School of Medicine faculty and students are leading the way in developing novel applications of AI to recognize and diagnose diseases, including rare diseases, earlier when we can have the greatest impact,” noted Dr. Nancy J. Brown, Dean of Yale School of Medicine.
As researchers continue to refine and expand this technology, the potential for AI in healthcare—especially in the diagnosis of rare genetic disorders—appears increasingly promising. The ability to diagnose Marfan syndrome through a non-invasive, widely accessible method could significantly reduce the risk of life-threatening complications and improve the quality of life for those living with the condition.
The study’s findings are detailed in the article, Pilot study exploring artificial intelligence for facial-image-based diagnosis of Marfan syndrome, published in the journal Heliyon.
Reference:
Saksenberg, D., et al. (2024). Pilot study exploring artificial intelligence for facial-image-based diagnosis of Marfan syndrome. Heliyon. DOI: 10.1016/j.heliyon.2024.e33858
