## 4 Results
### Eye Movement and Visual Processing Analysis and Virtual Reality  
Non-invasive methodologies for detecting ASD characteristics are examined, focusing on differences in gaze patterns, attention, and visual processing between individuals with ASD and their neurotypical counterparts. This section underlines the effectiveness of eye-tracking as a diagnostic tool, utilizing scanpath trends, fixation metrics, and gaze heatmaps to identify early markers of ASD. [PERSON] et al. (2020) [6] conducted a study in which they examined the potential of eye-tracking data on web-based tasks to identify ASD by analyzing scanpath trends. The researchers implemented machine learning algorithms to process gaze patterns and identify deviations in visual attention, such as fixation duration and gaze shifts, among individuals with and without ASD. Methodologically, the study focused on collecting eye-tracking data while participants engaged with web-based visual stimuli, allowing for real-time data capture. Results showed that individuals with ASD exhibited significantly different scanpath trends, particularly in terms of fixation stability and gaze repetition, compared to neurotypical individuals. The model achieved high accuracy, as measured by area under the curve (AUC) and precision scores, indicating strong sensitivity and specificity in differentiating ASD from non-ASD gaze patterns. The authors concluded that scanpath trend analysis via web-based tasks is a practical and scalable method for ASD screening, enabling accessible assessments that could be used beyond clinical settings. Expanding on this approach, another study by [PERSON] et al. (2020) [53] examined specific eye movement metrics, including fixations, saccades, and dwell time, to further investigate attentional biases and social cue processing differences in individuals with ASD. This study applied machine learning techniques to analyze these visual processing elements, with data collected from tasks requiring social attention and engagement with complex visual stimuli. In the study, the researchers used a logistic regression approach as a benchmark to measure the performance metrics (precision, recall, F1-score, and accuracy) of their proposed scanpath trend analysis (STA) method. They compared the results of the STA approach against the logistic regression approach to evaluate which method provided better classification results for detecting autism based on eye movement data. Cross-validation was used to ensure the reliability of the model, which demonstrated high sensitivity and specificity in distinguishing ASD-related gaze behaviors from those of control groups. With accuracy validated through performance metrics, the study emphasized that eye-tracking data provide a robust, non-invasive diagnostic marker, supporting real-time ASD assessment. The authors concluded that eye movement analysis not only enhances diagnostic precision but also offers a dynamic way to observe social attention patterns, thus contributing valuable insights into ASD's unique visual processing characteristics. In another study by [PERSON] et al. (2023) [40], the aim was to visualize early markers of ASD in children through the use of fixation mapping and attention heatmaps. Eye movement data were collected from 13 children diagnosed with autism using advanced models like InceptionV3 and compact convolutional transformers (CCTs). The CCT model utilized a dual tokenizer and attention-based mechanisms to extract features from the images. Heatmaps were generated from the eye-tracking data to visually represent gaze patterns, highlighting areas of interest where participants focused their attention. This combination of eye-tracking data and heatmap visualization aimed to improve the accuracy of autism detection while minimizing computational complexity, ultimately creating an impartial tool for ASD screening.