By Shania Kennedy, Health IT Analytics
Researchers have developed and validated a deep-learning model capable of accurately predicting cerebral palsy using spontaneous movements in high-risk infants.
In a prognostic study published in JAMA Network Open, researchers described the development of a deep-learning (DL) model that can effectively predict cerebral palsy based on infants’ spontaneous movements at nine to 18 weeks of age.
According to the Centers for Disease Control and Prevention (CDC), cerebral palsy is a group of disorders that affect a person’s ability to move and maintain balance and posture. Cerebral palsy is the most common motor disability in childhood and is caused by abnormal brain development or damage to the developing brain that affects a person’s ability to control their muscles.
Along with problems related to movement and posture, cerebral palsy patients may also experience seizures, spine changes, joint problems, intellectual disability, and problems with vision, hearing, or speech. Early signs of cerebral palsy in children are usually delays in reaching motor or movement milestones, in addition to more age-specific symptoms.
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