## 5 Conclusions  
Collectively, the studies reviewed demonstrate the potential of ML and DL techniques for diagnosing NPH. Both traditional ML and DL approaches show high accuracy for differentiating NPH from other conditions using morphological measurements and medical images. Traditional ML algorithms like RF, LogReg, and SVM achieved classification accuracy between 70% and 95% for NPH. Feature selection helped identify the most predictive parameters for improving performance. DCNN achieved comparable accuracy and the ability to automatically extract features. However, interpretability remains a challenge. A hybrid ML-DL framework may combine the best attributes of both. While ML and DL show potential to transform NPH diagnosis through more efficient, accurate, and personalized analysis of data, limitations remain in interpretability, data requirements, and individual accuracy. Future research should evaluate hybrid frameworks that synergistically integrate techniques to maximize benefits while mitigating challenges. With improvements that address key issues, AI may improve the diagnosis and management of NPH.  
Conceptualization, L.R.M.-D. and H.F.P.-Q.; methodology, L.R.M.-D. and H.F.P.-Q.; software, L.R.M.-D. and H.F.P.-Q.; formal analysis, L.R.M.-D. and H.F.P.-Q.; investigation, L.R.M.-D.; resources, H.F.P.-Q.; data curation, L.R.M.-D.; writing--original draft preparation, L.R.M.-D.; writing--review and editing, H.F.P.-Q., G.X.G. and N.P.; visualization, L.R.M.-D.; supervision,H.F.P.-Q.; project administration, H.F.P.-Q.; funding acquisition, H.F.P.-Q., G.X.G. and N.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a Seed Grant from the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut.  
Author [PERSON] was employed by the company XingImaging LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
Medical Terms  
AD  
Alzheimer's Disease  
CSF  
Cerebrospinal Fluid  
DESH  
Hydrocephalus  
Idiopathic Normal Pressure Hydrocephalus  
NPH  
Normal Pressure Hydrocephalus  
PD  
Parkinson's Disease  
PSP  
Progressive Supranuclear Palsy  
Imaging and Analysis  
CT  
Crumeterized Tomography  
FLAIR  
Fluid-attenuated Inversion Recovery  
ICP  
Intracranial Pressure  
MRI  
Magnetic Resonance Imaging  
PC-MRI  
Phase-Contrast Magnetic Resonance Imaging  
ROC-AUC  
Receiver Operating Characteristic--Area Under the Curve  
ROI  
Artificial Intelligence Methods  
AI  
Artificial Intelligence  
ANN  
Artificial Neural Networks  
CNN  
Convolutional Neural Network  
DA  
Discriminant Analyses  
DCNN  
Deep Convolutional Neural Network  
DL  
Deep Learning  
DT  
Decision Trees  
EPO  
Emperor Penguin Optimization  
GBDT  
Gradient Boosting Decision Tree  
GaussNB  
Gaussian Naive Bayes  
LogReg  
Logistic Regression  
ML  
Machine Learning  
MLP  
Multilayer Perceptron  
NN  
Neural Networks  
RF  
Random Forest  
SVM  
Support Vector Machine  
XGB  
Technical Tools and Measurements  
FLIRT  
FSRIB's Linear Image Registration Tool  
FMRIB Software Library  
Morphological Geodesic Active Contours  
Morphological Chan-Vese  
Waikato Environment for Knowledge Analysis