## 3 Contributions of Artificial Intelligence to ASD Detection  
In addition, the identification of many brain abnormalities, including ASD, has been greatly facilitated by the quick development of neuroimaging methods. For example, MRI is a crucial noninvasive technique for assessing brain structure, white matter (WM) integrity, and functional activity. Structural MRI (sMRI) has been employed to delineate the morphological alterations in the brain associated with ASD, focusing on the shape and volume of various brain areas. Diffusion tensor imaging (DTI) evaluates anatomical connections and has revealed impaired microstructural white matter integrity in individuals with autism. Functional MRI (fMRI) depends on identifying dynamic physiological data from active cerebral areas. Assessing alterations in blood-oxygenation-level-dependent (BOLD) signals in different brain states (resting state or task-induced) can uncover functional architectural anomalies in the ASD population despite various MRI modalities demonstrating potential in differentiating people with ASD from healthy controls (HCs) [49]. For instance, [PERSON] et al. [50] used features derived from ROI-based resting-state FC metrics to distinguish ASD from HCs. They used an SVM algorithm, exposing an enhancement in predictive accuracy  
Figure 2: PRISMA flow diagram illustrating the study-selection process. corresponding to an enhancement in participant numbers. Later, [PERSON] and [PERSON] [51] relied on SVM and a linear discriminant analysis (LDA) to categorize the data, and PCA was employed to reduce the noise characteristics based on graph theory with DTI-based connection characteristics and classify ASD from HCs. The best performance was determined using two PCA features and the SVM algorithm. Furthermore, EEG captures the brain's electrical activity by measuring the electrical impulses of various frequencies utilized by neurons for communication via electrodes affixed to the scalp. EEG-based diagnosis can offer better-tailored interventions by identifying diverse neurophysiological traits in autistic patients. EEG data analysis enables the detection of aberrant synchronous neural activity in children with ASD. The researcher investigated the amalgamation of EEG with AI methodologies to improve the identification and categorization of ASD [52]. For instance, [PERSON] et al. [53] utilized traditional statistical methods with classical ML techniques based on EEG data to classify distinct features or attributes linked to ASD. Researchers also combined statistical and ML approaches to enhance the classification of ASD. Moreover, eye tracking (ET) is a noninvasive technique for recording a person's gaze positions in real-time, which allows us to examine a user's eye movements or the focus point of an individual. It is a process of assessing the point of gaze or the location of eyes and gathering the eye features from an individual. The recorded data containing fixation amounts, first fixation, and fixation duration can be examined using visual analytic methods to review and obtain the eye features. It utilizes a visual analytic procedure to enhance the visualization of general visual problem-solving [54]. For example, [PERSON] et al. [55] used eye movement data to apply ML techniques for early detection of ASD gathered from individuals as they view different types of faces (real and artificial). Notably, it examined how the gaze patterns of individuals can be assessed when they inspect both real human faces and artificial faces to classify key markers of ASD. Researchers have relied on AI techniques for the early identification of ASD based on facial photos. Facial images may be the most reliable diagnosis technique because every child with ASD differs from a normal child based on facial features. Researchers from the University of Missouri discovered that children with autism exhibit specific facial characteristics, like wide-set eyes and a broad upper face.