机器学习在儿科创伤性脑损伤（pTBI）中的应用：文献系统评价。
Applications of machine learning in pediatric traumatic brain injury (pTBI): a systematic review of the literature.
机构信息
Department of Neurosurgery, University Hospital of Ioannina, Ioannina, Greece.
Medical School, University of Ioannina, Ioannina, Greece.
出版信息
Neurosurg Rev. 2024 Oct 5;47(1):737. doi: 10.1007/s10143-024-02955-3.
OBJECTIVE
Pediatric traumatic brain injury (pTBI) is a heterogeneous condition requiring the development of clinical decision rules (CDRs) for the optimal management of these patients. Machine learning (ML) is a novel artificial intelligence (AI) predictive tool with various applications in modern neurosurgery, including the creation of CDRs for patients with pTBI. In the present study, we summarized the current literature on the applications of ML in pTBI.
METHODS
A systematic review was conducted following the PRISMA guidelines. The literature search included PubMed/MEDLINE, SCOPUS, and ScienceDirect databases. We included observational or experimental studies focusing on the applications of ML in patients with pTBI under 18 years of age.
RESULTS
A total of 18 articles were included in our systematic review. Of these articles, 16 were retrospective cohorts, 1 was a prospective cohort, and 1 was a case-control study. Of these articles, ten concerned ML applications in predicting the outcome of pTBI patients, while 8 reported applications of ML in predicting the need for CT scans. Artificial Neuronal Network (ANN) and Random Forest (RF) were the most commonly utilized models for the creation of predictive algorithms. The accuracy of the ML algorithms to predict the need for CT scan in pTBI cases ranged from 0.790 to 0.999, and the Area Under Curve (AUC) ranged from 0.411 (95%CI: 0.354-0.468) to 0.980 (95%CI: 0.950-1.00). The model with the maximum accuracy to predict the need for CT scan was a Deep ANN model, while the model with the maximum AUC was Ensemble Learning. The model with the maximum accuracy to predict the outcome (favorable vs. unfavorable) of patients with TBI was a support vector machine (SVM) model with 94.0% accuracy, whereas the model with the highest AUC was an ANN model with an AUC of 0.991.
CONCLUSION
In the present systematic review, conventional and novel ML models were utilized to either predict the presence of intracranial trauma or the prognosis of children with pTBI. However, most of the reported ML algorithms have not been externally validated and are pending further research.
目的
小儿创伤性脑损伤（pTBI）是一种异质性疾病，需要制定临床决策规则（CDR）来优化这些患者的治疗。机器学习（ML）是一种新的人工智能（AI）预测工具，在现代神经外科中有多种应用，包括为 pTBI 患者制定 CDR。本研究总结了目前关于 ML 在 pTBI 中的应用的文献。
方法
根据 PRISMA 指南进行系统评价。文献检索包括 PubMed/MEDLINE、SCOPUS 和 ScienceDirect 数据库。我们纳入了重点关注 ML 在 18 岁以下 pTBI 患者中应用的观察性或实验性研究。
结果
我们的系统评价共纳入 18 篇文章。其中 16 篇为回顾性队列研究，1 篇为前瞻性队列研究，1 篇为病例对照研究。其中 10 篇文章涉及 ML 在预测 pTBI 患者预后方面的应用，8 篇报告了 ML 在预测 CT 扫描需求方面的应用。人工神经网络（ANN）和随机森林（RF）是创建预测算法最常用的模型。ML 算法预测 pTBI 患者 CT 扫描需求的准确性范围为 0.790 至 0.999，曲线下面积（AUC）范围为 0.411（95%CI：0.354-0.468）至 0.980（95%CI：0.950-1.00）。预测 CT 扫描需求的最大准确性的模型是深度 ANN 模型，而最大 AUC 的模型是集成学习。预测 TBI 患者预后（有利与不利）的最大准确性模型是支持向量机（SVM）模型，准确性为 94.0%，而 AUC 最高的模型是 ANN 模型，AUC 为 0.991。
结论
在本系统评价中，常规和新型 ML 模型用于预测颅内创伤的存在或 pTBI 儿童的预后。然而，大多数报告的 ML 算法尚未经过外部验证，有待进一步研究。
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