Fetal alcohol spectrum disorder diagnosed by code, eventually, maybe, if accuracy improves
Machine-learning algorithms can help doctors diagnose children affected by fetal alcohol spectrum disorder, according to fresh research.
Fetal alcohol spectrum disorder (FASD) describes a range of conditions that are caused when a woman consumes excessive amounts of booze during her pregnancy. Such benders can impact the development of a fetus, causing the child to be born with impaired visual or hearing, learning difficulties, or health issues affecting the bones, kidneys or heart.
Diagnosing issues stemming from FASD is tricky. It’s not straightforward like a simple blood test or screening. It’s a time consuming process that requires seeing a range of health specialists, including a physician, psychologist, facial dysmorphologist and occupational therapist to look for telltale symptoms.
Researchers from the University of Southern California (USC) and Duke University in the US, and Queen’s University, Canada, have developed a tool that could make diagnosis easier.
