Artificial Intelligence (AI) machine learning is increasingly used as a diagnostic tool for healthcare, biotechnology research, medical care and the life sciences intelligence. A new study published today in The Lancet Digital Health by researchers at the Children’s National Hospital in Washington, DC unveils an AI deep learning tool that can detect the risk of genetic syndromes in children with an accuracy of 88 percent.
“Genetic syndromes can be associated with severe cardiovascular, immune, endocrine, and neurodevelopmental risks, and thus have an impact on the quality of life of patients and their families,” wrote the researchers affiliated with the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital in Washington, DC, and George Washington University.
“Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services,” the scientists wrote. “We, therefore, aimed to develop and evaluate a machine learning-based screening technology using facial photographs to evaluate a child’s risk of presenting with a genetic syndrome for use at the point of care.”
“In this retrospective study, we developed a facial deep phenotyping technology based on deep neural networks and facial statistical shape models to screen children for genetic syndromes,” wrote the researchers.
“Facial appearance is key in a geneticist’s evaluation of people with suspected genetic syndromes,” explained the researchers. “However, primary care physicians, who are not trained to identify dysmorphology in diverse populations, often miss subtle indicators of genetic conditions. Additionally, cautious clinicians might refer healthy children with an atypical facial appearance to expensive and unnecessary genetic evaluations.”
The researchers reported that their deep learning artificial intelligence solution could detect the genetic syndrome with a high accuracy of 88 percent for the general population.
“This provides a substantial improvement over the reported accuracy of trained pediatricians to identify well studied conditions such as Down syndrome (64% accuracy from physical examination),” reported the researchers. “Our results demonstrate the feasibility of our method, and the potential to improve the early detection of genetic syndromes.”
“This genetic screening technology could support early risk stratification at the point of care in global populations, which has the potential to accelerate diagnosis and reduce mortality and morbidity through preventive care,” the researchers concluded.
