### Background
#### 1.1.3 Artificial Intelligence  
There has been a growing interest in the application of AI [XE \"AI\" \(\dagger\) \"_Artificial Intelligence_\"] techniques, including machine learning (ML [XE \"ML\" \(\dagger\) \"_machine learning_\"]) and deep learning (DL [XE \"DL\" \(\dagger\) \"_deep learning_\"]), for the diagnosis of NPH and the prediction of surgical response [XE \"DL\" \(\dagger\) \"_Deep Learning_\"]. AI refers to the development of intelligent systems that can perform tasks typically requiring human intelligence. It encompasses various techniques and algorithms aimed at enabling machines to perceive, reason, learn, and make decisions [18]. Two fundamental concepts within AI are ML and DL. ML is a subset of AI that focuses on algorithms and statistical models that enable computers to learn from data and make pre-dictions or decisions without being explicitly programmed [19]. It involves the training of models on large datasets, where patterns and relationships are automatically discovered. Common ML applications include regression, classification, clustering, and reinforcement learning. ML encompasses traditional methods, such as SVM, Decision Trees (DT [XE \"DT\" \(\dagger\) \"_Decision Trees_\"]), and shallow neural networks (NN [XE \"NN\" \(\dagger\) \"_neural networks_\"]) [XE \"NN\" \(\dagger\) \"_Neural Networks_\"]). These models are trained using algorithms that optimize their performance based on objective functions and evaluation metrics [18]. DL is a specialized form of ML, inspired by the structure and function of the human brain's neural networks. It involves the use of artificial neural networks composed of multiple layers of interconnected nodes (neurons) [20]. These networks are capable of automatically learning hierarchical representations of data by progressively extracting complex features at each layer. DL has demonstrated exceptional performance in areas such as image and speech recognition, natural language processing, and autonomous driving [9]. More specifically, ML algorithms are designed to automatically learn patterns and make predictions based on features obtained from the data [21], while DL algorithms utilize layers of Artificial Neural Networks (ANN [XE \"ANN\" \(\dagger\) \"Artificial Neural Networks\"]) to extract hierarchical representations from raw data [18]. As noted, feature extraction plays a crucial role in training ML models. In the context of NPH, features can be categorized into structural, functional, and clinical features. These features describe the physical structure of the brain and are derived from medical images. Prominent structural features used for NPH diagnosis include ventricle size and shape, CSF volume, and white-matter integrity. Various image processing techniques, such as segmentation and shape analysis, are used to extract these features from brain images [22; 23; 24]. Clinical features include demographic information (e.g., age, sex), specific symptoms associated with NPH (e.g., gait disturbances, cognitive impairments), and relevant medical history [25]. Functional features are often derived from various imaging techniques and physiological measures, allowing researchers to examine the functional aspects of neurodegenerative diseases (e.g., resting state functional connectivity, task-based functional activation). Other functional features are obtained from a cognitive evaluation (e.g., cognitive functions, memory, attention, language, and executive functions). These features provide functional measures of cognitive abilities affected by neurodegenerative diseases [26]. Some studies combine features from several categories (structural, functional, and clinical). These multimodal methods typically enhance the detection, diagnosis, or seg mentation of NPH. Although ML methods have been widely used, DL algorithms have recently shown remarkable success in various medical imaging tasks, including NPH diagnosis [27; 28; 29]. In this review, we have considered both ML and DL approaches to explore their potential in NPH diagnosis [28; 30].