Dai Yuwei, Zhong Zhusi, Qin Yan, Wang Yuli, Yu Guangdi, Kobets Andrew, Swenson David W, Boxerman Jerrold L, Li Gang, Robinson Shenandoah, Bai Harrison, Yang Li, Liao Weihua, Jiao Zhicheng
Department of Neurology, Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.
Hum Brain Mapp. 2025 Oct 1;46(14):e70363. doi: 10.1002/hbm.70363.
Predictive tools for stratifying neonatal hydrocephalus into low- and high-risk groups for cerebrospinal fluid (CSF) diversion are currently lacking. We developed and validated an artificial intelligence (AI) model that integrates multimodal imaging and clinical data to predict CSF diversion needs. The development cohort included 116 neonates with suspicion of raised intracranial pressure (ICP) from a Chinese tertiary referral hospital (80 with intracranial pressure > 80 mm HO, 36 with intracranial pressure ≤ 80 mm HO). The external validation cohort consisted of 21 neonates with hydrocephalus from an American medical center, categorized by etiology: prenatal myelomeningocele (MMC) closure (n = 5), postnatal MMC closure (n = 6), and post-hemorrhagic hydrocephalus (PHH) (n = 10). Inclusion criteria required available MRI and complete clinical follow-up to confirm CSF diversion outcomes. The primary outcome was the need for CSF diversion. Model performance was assessed using under the receiver operating characteristics curve (AUC), sensitivity, and specificity. The hybrid AI model achieved an AUC of 0.824 in the development cohort in predicting raised ICP, outperforming both the clinical-only model (AUC 0.528, p < 0.001) and the image-only model (AUC 0.685, p = 0.007). In the external validation cohort, the fused MRI-based model achieved an AUC of 0.808. The model correctly predicted CSF diversion in 4/5 prenatal MMC, 4/6 postnatal MMC, and 9/10 PHH cases. The AI model demonstrated robust performance in predicting the need for CSF diversion, particularly in PHH cases, and has the potential to assist decision-making, especially in settings with limited pediatric neurosurgical expertise. Future work should focus on further refining model performance for complex etiologies such as MMC-associated hydrocephalus.
目前缺乏用于将新生儿脑积水分为脑脊液（CSF）分流低风险和高风险组的预测工具。我们开发并验证了一种人工智能（AI）模型，该模型整合多模态成像和临床数据以预测CSF分流需求。开发队列包括来自中国一家三级转诊医院的116名怀疑颅内压（ICP）升高的新生儿（80名颅内压> 80 mm HO，36名颅内压≤ 80 mm HO）。外部验证队列由来自美国一家医疗中心的21名脑积水新生儿组成，按病因分类：产前脊柱裂（MMC）闭合（n = 5）、产后MMC闭合（n = 6）和出血后脑积水（PHH）（n = 10）。纳入标准要求有可用的MRI和完整的临床随访以确认CSF分流结果。主要结局是CSF分流的需求。使用受试者操作特征曲线（AUC）、敏感性和特异性评估模型性能。混合AI模型在开发队列中预测ICP升高时的AUC为0.824，优于仅临床模型（AUC 0.528，p < 0.001）和仅图像模型（AUC 0.685，p = 0.007）。在外部验证队列中，基于融合MRI的模型AUC为0.808。该模型在4/5的产前MMC、4/6的产后MMC和9/10的PHH病例中正确预测了CSF分流。AI模型在预测CSF分流需求方面表现出强大性能，特别是在PHH病例中，并且有潜力协助决策，尤其是在儿科神经外科专业知识有限的环境中。未来的工作应集中于进一步优化针对诸如MMC相关脑积水等复杂病因的模型性能。
