Utility of Machine Learning in the Management of Normal Pressure Hydrocephalus: A Systematic Review

In the past decade, many machine learning (ML) models have been used in the management of normal pressure hydrocephalus (NPH). This study aims at systematically reviewing those ML models.

The PubMed, Embase, and Web of Science databases were searched for studies reporting applications of ML in NPH. Quality assessment was performed using Prediction model Risk Of Bias ASsessment Tool (PROBAST) and Transparent Reporting of a multivariable predication model for Individual Prognosis Or Diagnosis (TRIPOD) adherence reporting guidelines, and statistical analysis was performed with the level of significance of <0.05.

A total of 22 studies with 53 models were included in the review, of which the convolutional neural network was the most used model. Inputs used to train various models included clinical features, computed tomography scan, magnetic resonance imaging, intracranial pulse waveform characteristics, and perfusion infusion. The overall mean accuracy of the models was 77% (highest for the convolutional neural network, 98%, while lowest for decision tree, 55%; P = 0.176). There was a statistically significant difference in the accuracy and area under the curve of diagnostic and interventional models (accuracy: 83.4% vs. 69.4%, area under the curve: 0.882 vs. 0.729; P < 0.001). Overall, 59.09% (n = 13) and 81.82% (n = 18) of the studies had high-risk bias and high-applicability, respectively, on PROBAST assessment; however, only 55.15% of the studies adhered to the TRIPOD statement.

Though highly accurate, there are many challenges to current ML models necessitating the need to standardize the ML models to enable comparison across the studies and enhance the NPH decision-making and care.