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Abstract #1544

Automating early prediction of cerebral palsy: A transfer learning model for infant MRI analysis

Zhen Jia1,2,3,4, Tingting Huang2,3,4, Yitong Bian2,3,4, Xianjun Li2,3,4, and Jian Yang1,2,3,4
1School of Future Technology, Xi'an Jiaotong University, Xi'an, China, 2Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an, China, 3Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, China, 4Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China

Synopsis

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.

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Keywords