While many babies are born without issues on a daily basis, there are quite a few who are born early and with some complications. Those babies end up in the Neonatal Intensive Care Unit (NICU), which is where Dr. Sabrina Malone Jenkins works. The Neonatologist at University of Utah Health and Intermountain Primary Children's Hospital said, "A lot of the time we don't know what the underlying condition is and there is a wide variety of causes and rarely are any of them the same." Researchers say on a global scale about seven million infants are born with serious genetic disorders each year and it can be tough for doctors to treat them if they're not sure what's wrong. Dr. Mark Yandell, a Professor in the Department of Human Genetics at the University of Utah said, "It's estimated that about 20% of the newborns in a high-intensity newborn intensive care unit have some form of genetic disease."

Artificial Intelligence (AI) is helping doctors diagnose rare diseases in babies and Utah doctors are using the tool with the hope of saving more …

WASHINGTON D.C. [USA]: According to a recent study, a new artificial intelligence technology can accurately identify rare genetic disorders using a photograph of a patient's face. Named DeepGestalt, the AI technology outperformed clinicians in identifying a range of syndromes in three trials and could add value in personalised care, CNN reported. The study was published in the journal Nature Medicine. According to the study, eight per cent of the population has disease with key genetic components and many may have recognisable facial features. The study further adds that the technology could identify, for example, Angelman syndrome, a disorder affecting the nervous system with characteristic features such as a wide mouth with widely spaced teeth etc. Speaking about it, Yaron Gurovich, the chief technology officer at FDNA and lead researcher of the study said, "It demonstrates how one can successfully apply state of the art algorithms, such as deep learning, to a challenging field where the available data is small, unbalanced in terms of available patients per condition, and where the need to support a large amount of conditions is great."