LATIDIA · Investigación

Por qué enseñar a la IA sobre la simetría podría hacer que el diagnóstico prenatal sea más fiable

Un modelo desarrollado por investigadores del MIT y el Boston Children's Hospital ajusta las exploraciones sobre la marcha para rastrear el movimiento fetal, lo que podría evitar que las familias repitan las visitas al hospital.

La noticia

Despite millions of years of evolution, the development of the human fetus remains a complex process that is vulnerable to genetic, environmental, and metabolic disruptions. These disruptions could result in developmental abnormalities, starting with one of the first organs to begin developing in the fetus that continues to develop after birth: the brain. In a routine prenatal ultrasound, the sonographer will flag possible brain abnormalities for additional magnetic resonance imaging (MRI). But imperfect imaging can result in missed or incorrect diagnoses. To assist with the early detection and treatment of these abnormalities, researchers at MIT, Boston Children’s Hospital, and Inria have developed and trained a deep learning model called E(3)-Pose