MRI-based prediction of cerebral palsy risk in infants aged 6 months to 2 years: a deep learning approach

Motivation: Early prediction of cerebral palsy (CP) in infants plays a pivotal role in facilitating tailored rehabilitation treatment. Goal(s): We hope to achieve early prediction of CP in infants aged 6 months to 2 years old based on MRI and deep learning technology. Approach: We introduce a novel neural network model, known as the "Cerebral Palsy Brain Constraint Residual Network" (CPBC-Resnet), for the automatic prediction of CP risk based on MRI data. Results: The CPBC-Resnet model exhibits an impressive receiver operating characteristic area under the curve (AUC) of 0.9521, achieving a sensitivity of 94.12% and a specificity of 100%. Impact: This study streamlines cerebral palsy (CP) imaging diagnostics, reducing physician training costs, and expanding the reach of CP diagnostic technology. It promotes early CP diagnosis and intervention, particularly in areas with underdeveloped medical standards, contributing to overall child health improvement.