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Updated: Jun 20, 2026
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Published on: April 13, 2013
Early prediction of progressive cerebral contusion using a deep transfer learning-enhanced multimodal nomogram
Wanqin Yang1, Shanshan Qian1, Ping Zhao2
1Department of Emergency, The Third People's Hospital of Hefei, Hefei Third Clinical College of Anhui Medical University, Hefei, China.
Objectives:
We developed a multimodal fusion model combining clinical data and deep transfer learning for early progressive cerebral contusion (PCC) prediction, providing precise clinical support for treatment decisions.
Methods:
Using a single-center retrospective cohort design, we analyzed 196 cerebral contusion patients between January 2022 and June 2024. PCC was characterized by a contusion volume increase of at least 30 % on CT scans within 24 h. Patients were categorized into a progression group (n=98) and a non-progression group (n=98). The dataset was split into a training coh59 participants, maintaining a 7:3 ratort of 137 participants and a validation cohort of io. A nomogram was developed by combining ResNet-50-based deep transfer learning features with clinical variables. Model performance was assessed through ROC curves, calibration plots, and decision curve analysis, while Grad-CAM was used to evaluate interpretability.
Results:
The integrated nomogram demonstrated superior performance with AUC values of 0.999 (95 % CI: 0.998-1.000) in the training cohort and 0.972 (95 % CI: 0.939-1.000) in the validation cohort, surpassing the standalone DTL and clinical models. Grad-CAM demonstrated accurate lesion localization.
Conclusions:
The multimodal fusion model integrating DTL and clinical features shows excellent predictive performance and significant clinical value in early PCC prediction.