Deep learning-based assessments help in early detection of Cerebral Palsy in infants

Cerebral palsy (CP) is the most common physical disability in children, producing functional limitation and co-occurring impairments because of injury to the developing brain. Cerebral palsy is typically diagnosed between ages 12 and 24 months, and milder forms of CP may be diagnosed even later in childhood.

Early identification of infants with a high risk of CP is important to provide targeted follow-up and interventions during infancy when neuroplasticity is high, improve access to community services to minimize complications, and reassure parents of infants at high risk if their children are unlikely to develop CP.

So for this reason, a recent study published in the JAMA Network was conducted to develop and assess the external validity of a novel deep learning–based method to predict CP based on videos of infants' spontaneous movements at 9 to 18 weeks' corrected age.

This prognostic study of a deep learning–based method to predict CP at a corrected age of 12 to 89 months involved 557 infants with a high risk of perinatal brain injury who were enrolled in previous studies conducted at 13 hospitals. A total of 418 infants were randomly assigned to the model development sample, and 139 were randomly assigned to the external validation sample.

The results of the study showed that the deep learning method achieved higher accuracy than the conventional machine learning method but no significant improvement in accuracy was observed compared with the GMA tool.

The deep learning prediction model had higher sensitivity among infants with non ambulatory CP vs ambulatory CP and spastic bilateral CP vs spastic unilateral CP.

Therefore, in this prognostic study, a deep learning–based method for predicting CP at 9 to 18 weeks' corrected age had predictive accuracy on external validation, which suggests possible avenues for using deep learning–based software to provide objective early detection of CP in clinical settings.

Reference: Groos D, Adde L, Aubert S, et al. Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk. JAMA Network Open. 2022;5(7):e2221325.