Keywords: Diagnosis/Prediction, Diagnosis/Prediction
Motivation: Clinical diagnosis of cerebral palsy (CP) is often delayed, costly, and limited in accuracy, highlighting the need for a low-cost, automated, early diagnosis approach.
Goal(s): This study develops a deep transfer learning (TL) model for early CP prediction in infants aged 6 months to 2 years.
Approach: The CP-TL model, based on ResNet-18 with two pre-trained weights, was tested on various dataset combinations. Performance was evaluated using accuracy, sensitivity, specificity, and AUC.
Results: The single-center dataset with pre-trained weight 1 achieved the best performance (accuracy: 92.19%, AUC: 0.9655), while weight 2 excelled with multicenter data, highlighting the need for weight selection.
Impact: This model demonstrates the potential of deep transfer learning for early CP prediction, offering reliable support for early intervention and rehabilitation planning in infants aged 6 months to 2 years, with significant clinical application value.
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