• Rafael Tiza Fernandes, Filipe Wolff Fernandes, Mrinmoy Kundu, Daniele S C Ramsay, Ahmed Salih, Srikar N Namireddy, Dragan Jankovic, Darius Kalasauskas, Malte Ottenhausen, Andreas Kramer, Florian Ringel, and Santhosh G Thavarajasingam.
• Imperial Brain & Spine Initiative, Imperial College London, London, United Kingdom; Department of Neurosurgery, ULS São José, Lisbon, Portugal.
• World Neurosurg. 2024 Oct 10.

BackgroundIdiopathic 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.MethodsWe 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.ResultsOf 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.ConclusionsWhile 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.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

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