Artificial Intelligence Approaches for the Detection of Normal Pressure Hydrocephalus: A Systematic Review

• Published in: Applied sciences Vol. 15; no. 7; p. 3653
• Main Authors: Mercado-Diaz, Luis R., Prakash, Neha, Gong, Gary X., Posada-Quintero, Hugo F.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2025
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Artificial intelligence; Cognitive ability; Computational linguistics; Decision trees; Deep learning; Disease; Hydrocephalus; Language processing; Machine learning; Medical imaging; Medical imaging equipment; Natural language interfaces; Nervous system diseases; Neural networks; normal pressure hydrocephalus; Surgery; Systematic review; Urinary incontinence; United States; Connecticut; Taiwan
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15073653

Normal pressure hydrocephalus (NPH) is a neurological disorder characterized by altered cerebrospinal fluid accumulation in the brain’s ventricles, leading to symptoms such as gait disturbance and cognitive impairment. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), shows promise in diagnosing NPH using medical images. In this systematic review, we examined 21 papers on the use of AI in detecting NPH. The studies primarily focused on differentiating NPH from other neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease. We found that traditional ML methods like Support Vector Machines, Random Forest, and Logistic Regression were commonly used, while DL methods, particularly Deep Convolutional Neural Networks, were also widely employed. The accuracy of these approaches varied, ranging from 70% to 95% in differentiating NPH from other conditions. Feature selection techniques were used to identify relevant parameters for diagnosis. MRI scans were more frequently used than CT scans, but both modalities showed promise. Evaluation metrics like Dice similarity coefficients and ROC-AUC were the most typical metrics of model performance. Challenges in implementing AI in clinical practice were identified, and the authors suggested that a hybrid deep-traditional ML framework could enhance NPH diagnosis. Further research is needed to maximize the benefits of AI while addressing limitations.