Construction and effectiveness test of multimodal data fusion prediction model for intracranial infection after severe craniocerebral injury in children based on deep learning

The multimodal data fusion prediction model integrating deep learning, radiomics, and clinical data demonstrates excellent performance in the early postoperative monitoring and identification of intracranial infection in pediatric severe TBI.

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Publication Details

Journal
BMC Neurology
Published
2025-11-22
DOI
https://doi.org/10.1186/s12883-025-04502-z
Primary Topic
Traumatic Brain Injury and Neurovascular Disturbances
Type
article
Field-Weighted Citation Impact
0.00
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article

Construction and effectiveness test of multimodal data fusion prediction model for intracranial infection after severe craniocerebral injury in children based on deep learning

Jianbin Weng, Zhipeng Shen, Wenbo Zhang, Peiliang Zhang
BMC Neurology
Traumatic Brain Injury and Neurovascular Disturbances
article

Construction and effectiveness test of multimodal data fusion prediction model for intracranial infection after severe craniocerebral injury in children based on deep learning

Jianbin Weng, Zhipeng Shen, Wenbo Zhang, Peiliang Zhang
article en

Abstract

The multimodal data fusion prediction model integrating deep learning, radiomics, and clinical data demonstrates excellent performance in the early postoperative monitoring and identification of intracranial infection in pediatric severe TBI.

BMC NeurologyVol. 25(1)
Children's Hospital of Zhejiang University (CN)
Openalex Percentile: Top 51%
Traumatic Brain Injury and Neurovascular Disturbances
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