## 4 Results
### EEG-Based Approaches, IoT, Smart Devices, and Serious Games
This app employed a convolutional neural network (CNN) to classify ASD-related traits, replacing traditional scoring methodologies with CNN predictions for greater accuracy. Caregivers or clinicians provided input data, which the app analyzed to detect ASD indicators. Tested on a comprehensive dataset, Autism AI demonstrated superior sensitivity, specificity, and overall accuracy compared to conventional screening tools. The study concluded that Autism AI offers a practical and accessible solution for ASD screening, reducing diagnostic subjectivity and making screening more widely available, especially in settings where traditional methodologies may be impractical or less efficient. Similarly, [PERSON] et al. (2021) developed a machine learning-based device, which employs a gradient-boosted decision-tree algorithm to analyze 64 behavioral features derived from three distinct sources: caregiver questionnaires, 2 to 4 min of home videos reviewed by trained video analysts, and primary care physician (PCP) questionnaires. The device's performance was evaluated by comparing its diagnostic results with the consensus of specialist clinicians, who used a modified Child Autism Rating Scale (CARS-2) and DSM-5 criteria [16]. These specialists included child psychiatrists, child psychologists, pediatric neurologists, and developmental behavioral pediatricians with expertise in ASD diagnosis [37]. The device demonstrated promising results, achieving a positive predictive value (PPV) of 80.8% (95% CI, 70.3-88.8), a negative predictive value (NPV) of 98.3% (90.6-100), sensitivity of 98.4% (91.6-100), and a specificity of 78.9% (67.6-87.7) for determinate cases. Importantly, the device's performance was consistent whether the PCP used it remotely or in person, highlighting its adaptability for telemedicine applications. These findings suggest that such technology could enable PCPs to efficiently, accurately, and equitably diagnose ASD in children aged 18 to 72 months, facilitating earlier intervention and reducing reliance on specialist referrals. While these results are preliminary, they underscore the potential of integrating machine learning-based diagnostic tools into primary care settings to enhance the accessibility and timeliness of ASD diagnosis. In a large-scale study, [PERSON] et al. (2019) [33] conducted a phase 3 diagnostic evaluation of a smart tablet-based serious game designed to identify ASD-specific behaviors in children aged 3 to 5 across Sweden and the United Kingdom. The game was crafted to elicit responses that reveal social and communicative traits commonly linked to ASD. During game-play, data on reaction times, eye movements, and behavioral response patterns were collected and analyzed by a machine learning model to identify ASD indicators. This study used sensitivity and specificity metrics to measure accuracy, yielding high reliability in distinguishing between ASD and non-ASD participants. The study concluded that such serious games offer an interactive and scalable approach to ASD screening, allowing children to engage in a playful, stress-free setting while providing clinicians with valuable behavioral data for early detection. [PERSON] et al. (2024) proposed an IoT-based framework for automated assessment and reporting light sensitivities in children with autism spectrum disorder (ASD), addressing the limitations of traditional subjective and time-intensive methods. The system, called IoT-ISAS, utilizes IoT, computer vision, and data mining techniques to assess visual sensitivities to light (color and illumination). It operates in two modes: child control mode (CCM), which uses a preference-based approach, and system control mode (SCM), which tracks emotional responses during sensory stimulation sessions. Tested on a sample of 20 children with ASD, the IoT-ISAS achieved a 95% agreement rate in CCM and 90% in SCM when compared to practitioner assessments. The framework significantly reduces the time required for diagnosing light sensitivities while providing objective and reliable results.