Fernandes Rafael Tiza, Fernandes Filipe Wolff, Kundu Mrinmoy, Ramsay Daniele S C, Salih Ahmed, Namireddy Srikar N, Jankovic Dragan, Kalasauskas Darius, Ottenhausen Malte, Kramer Andreas, Ringel Florian, Thavarajasingam Santhosh G
Imperial Brain & Spine Initiative, Imperial College London, London, United Kingdom; Department of Neurosurgery, ULS São José, Lisbon, Portugal.
Imperial Brain & Spine Initiative, Imperial College London, London, United Kingdom; Department of Neurosurgery, Hannover Medical School, Hannover, Germany.
World Neurosurg. 2024 Dec;192:e281-e291. doi: 10.1016/j.wneu.2024.09.087. Epub 2024 Oct 10.
Idiopathic normal pressure hydrocephalus (iNPH) is a reversible cause of dementia, typically treated with shunt surgery, although outcomes vary. Artificial intelligence (AI) advancements could improve predictions of shunt response (SR) by analyzing extensive datasets.
We conducted a systematic review to assess AI's effectiveness in predicting SR in iNPH. Studies using AI or machine learning algorithms for SR prediction were identified through searches in MEDLINE, Embase, and Web of Science up to September 2023, adhering to Synthesis Without Meta-Analysis reporting guidelines.
Of 3541 studies identified, 33 were assessed for eligibility, and 8 involving 479 patients were included. Study sample sizes varied from 28 to 132 patients. Common data inputs included imaging/radiomics (62.5%) and demographics (37.5%), with Support Vector Machine being the most frequently used machine learning algorithm (87.5%). Two studies compared multiple algorithms. Only 4 studies reported the Area Under the Curve values, which ranged between 0.80 and 0.94. The results highlighted inconsistency in outcome measures, data heterogeneity, and potential biases in the models used.
While AI shows promise for improving iNPH management, there is a need for standardized data and extensive validation of AI models to enhance their clinical utility. Future research should aim to develop robust and generalizable AI models for more effective diagnosis and management of iNPH.
特发性正常压力脑积水（iNPH）是痴呆的一个可逆病因，通常采用分流手术治疗，但其疗效各异。人工智能（AI）的进步可通过分析大量数据集来改善对分流反应（SR）的预测。
我们进行了一项系统综述，以评估AI在预测iNPH的SR方面的有效性。通过检索截至2023年9月的MEDLINE、Embase和科学网，遵循非荟萃分析综合报告指南，确定了使用AI或机器学习算法进行SR预测的研究。
在识别出的3541项研究中，33项被评估是否符合纳入标准，8项涉及479例患者的研究被纳入。研究样本量从28例到132例患者不等。常见的数据输入包括影像学/放射组学（62.5%）和人口统计学数据（37.5%），支持向量机是最常用的机器学习算法（87.5%）。两项研究比较了多种算法。只有4项研究报告了曲线下面积值，范围在0.80至0.94之间。结果突出了结局指标的不一致性、数据异质性以及所用模型中的潜在偏倚。
虽然AI在改善iNPH管理方面显示出前景，但需要标准化数据并对AI模型进行广泛验证，以提高其临床实用性。未来的研究应旨在开发强大且可推广的AI模型，以更有效地诊断和管理iNPH。
