Scholarlink

Objective <p>To develop and validate a multimodal data fusion prediction model based on deep learning for the early postoperative identification of intracranial infection in pediatric patients with severe traumatic brain injury (TBI).</p> Methods <p>A total of 203 pediatric TBI patients who underwent surgery at Children’s Hospital, Zhejiang University School of Medicine between March 2022 and May 2025 were included as the internal validation cohort. These patients were stratified into infection group (46 cases) and non-infection group (157 cases) based on the occurrence of postoperative infection. General clinical data were compared between the two groups, and multivariate logistic regression analysis was performed to identify risk factors for postoperative infection. Radiomic features and deep learning features were extracted and screened. Additionally, 101 pediatric patients who underwent surgery during the same period were selected as the temporal validation cohort (25 infected cases and 76 non-infected cases). Four predictive models were constructed for the internal temporal validation cohort: a radiomics model, a clinical model, a deep learning model, and a combined model. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, clinical decision curve analysis, and calibration curves.</p> Results <p>Statistically significant differences (<i>P</i> &lt; 0.05) were observed between the infection and non-infection groups in terms of operative duration, preoperative Glasgow Coma Scale (GCS) score, external ventricular drainage (EVD), incisional cerebrospinal fluid (CSF) leakage, incisional effusion, number of surgeries, hypersensitive C-reactive protein (hs-CRP), procalcitonin (PCT), and blood lactate levels. Logistic regression analysis identified EVD, incisional effusion, hs-CRP, incisional CSF leakage, and PCT as independent risk factors for postoperative intracranial infection in pediatric TBI patients (<i>P</i> &lt; 0.05). A total of 9 radiomic features and 20 deep learning features were retained. ROC and DCA analyses demonstrated that the combined prediction model exhibited superior predictive performance and greater clinical net benefit compared to the clinical, radiomics, and deep learning models alone.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月22日

查看全文
  • Peiliang Zhang,
  • Wenbo Zhang,
  • Jianbin Weng,
  • Zhipeng Shen

摘要

Objective

To develop and validate a multimodal data fusion prediction model based on deep learning for the early postoperative identification of intracranial infection in pediatric patients with severe traumatic brain injury (TBI).

Methods

A total of 203 pediatric TBI patients who underwent surgery at Children’s Hospital, Zhejiang University School of Medicine between March 2022 and May 2025 were included as the internal validation cohort. These patients were stratified into infection group (46 cases) and non-infection group (157 cases) based on the occurrence of postoperative infection. General clinical data were compared between the two groups, and multivariate logistic regression analysis was performed to identify risk factors for postoperative infection. Radiomic features and deep learning features were extracted and screened. Additionally, 101 pediatric patients who underwent surgery during the same period were selected as the temporal validation cohort (25 infected cases and 76 non-infected cases). Four predictive models were constructed for the internal temporal validation cohort: a radiomics model, a clinical model, a deep learning model, and a combined model. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, clinical decision curve analysis, and calibration curves.

Results

Statistically significant differences (P < 0.05) were observed between the infection and non-infection groups in terms of operative duration, preoperative Glasgow Coma Scale (GCS) score, external ventricular drainage (EVD), incisional cerebrospinal fluid (CSF) leakage, incisional effusion, number of surgeries, hypersensitive C-reactive protein (hs-CRP), procalcitonin (PCT), and blood lactate levels. Logistic regression analysis identified EVD, incisional effusion, hs-CRP, incisional CSF leakage, and PCT as independent risk factors for postoperative intracranial infection in pediatric TBI patients (P < 0.05). A total of 9 radiomic features and 20 deep learning features were retained. ROC and DCA analyses demonstrated that the combined prediction model exhibited superior predictive performance and greater clinical net benefit compared to the clinical, radiomics, and deep learning models alone.

Conclusion

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.