Keywords: Diagnosis/Prediction, Brain, Cerebral Palsy
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords