## 4 Results
### Neuroimaging and Brain Connectivity Analysis  
The role of neuroimaging and AI-based connectivity analysis in identifying ASD-specific differences in brain structure and connectivity is examined. Utilizing advanced techniques in structural and functional brain analysis, these approaches demonstrate how AI-enhanced neuroimaging tools can enhance diagnostic precision and expand our understanding of ASD's neuroanatomical features. The study by [PERSON] et al. (2024) [8] focused on using automated segmentation with structural MRI (sMRI) data to identify brain structural abnormalities in children with ASD. The researchers applied an AI-based segmentation tool to compare brain volumes in ASD children against neurotypical controls, paying particular attention to areas such as the right orbitofrontal cortex and the parahippocampal gyrus. These regions showed significant volume increases in children with ASD. To assess the predictive power of these findings, the researchers employed machine learning classifiers, including support vector machine (SVM) and AdaBoost models, achieving area under the curve (AUC) scores of 0.91 and 0.92, respectively. These high AUC values underscore the potential of AI-enhanced sMRI segmentation in providing quantifiable insights into ASD-specific brain structure changes. The study concluded that such methodologies could significantly contribute to clinical diagnostics by offering objective measurements of brain abnormalities associated with ASD. Building on the foundation of structural analysis, [PERSON] et al. (2023) [9] investigated the application of AI and a functional connectivity analysis of brain data to enhance the diagnosis of ASD. The methodology of the study involved several key steps to enhance the diagnosis of autism spectrum disorder (ASD) using resting-state functional MRI (rs-fMRI) data. Researchers collected data from the Autism Brain Imaging Data Exchange (ABIDE) dataset and performed a brain connectivity analysis using six different brain atlases to create connectivity matrices. To tackle the high dimensionality of the fMRI data, they employed a low-rank tensor approximation method for dimensionality reduction. A majority voting algorithm was then utilized to combine results from the various brain atlases, addressing subject variability. Finally, the study introduced the ALERT method, which was compared against state-of-the-art techniques, including traditional machine learning classifiers and deep learning methodologies, to evaluate its effectiveness in accurately diagnosing ASD. This comprehensive approach aimed to improve diagnostic accuracy by leveraging advanced computational techniques. The results demonstrated an ASD detection accuracy of 84.79% using a majority voting approach based on different brain atlases, with the Adaptive Low-Estimated Rank Tensor (ALERT) method outperforming traditional techniques. The findings underscore the significance of considering individual differences in brain connectivity and suggest that integrating various inputs and advanced analytical methodologies can improve diagnostic accuracy, ultimately contributing to more effective clinical tools for ASD diagnosis. In a complementary approach, [PERSON] et al. (2024) [44] investigated how brain imaging data could be used within an IoT framework for early ASD detection by applying transfer learning techniques to functional MRI data. In this study, the authors focused on identifying functional connectivity networks through the use of various brain atlas models, analyzing patterns that distinguish between ASD and neurotypical children. Using transfer learning, they processed correlation matrices derived from brain connectivity patterns, with cross-validation employed to assess diagnostic accuracy. The methodology began with the collection and preprocessing of brain imaging datasets to prepare them for analysis. Pre-trained neural network models were employed through transfer learning to identify patterns associated with ASD.