Game-Based Diagnosing of Children with Autism Spectrum Disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose: To develop a diagnostic application game targeting children, aiming to streamline the diagnostic process and facilitate early intervention in children with Autism Spectrum Disorder (ASD). Methods: This work was built around two key components: a gaming interface based on real-life scenarios that a child with ASD would encounter, and a Convolutional Neural Network (CNN) model that takes photographs of the children’s faces and determines whether they are autistic or not. Combining these two methodologies, we created a video game that categorizes the youngsters who play it as autistic or non-autistic based on their reactions and choices during the game scenarios and their facial structures (Aldridge et al., 2011). The scenarios in the game were inspired by diagnostic questionnaires used in clinics for diagnostic purposes (Sadek et al., 2020). We sought to add the cultural background influence in ASD diagnosis because several research studies have revealed that children from different cultures can have varied symptoms depending on their cultural background (Golson et al., 2021). We used the AQ-10 questionnaire (Allison et al., 2012) and distributed it to parents of autistic children to evaluate how they see their child and if social norms influence it. In addition, we spoke with a Turkish specialist who works with Turkish autistic children and included her thoughts on the game’s situations. Results: After experimenting with various models on the same dataset (Gerry, 2020), the efficientNet B3 model attained the highest accuracy of 87.5%. The ultimate results presented to the game’s player were a combination of the model results and the outcomes of the scenarios he chose throughout his play. If the player reacts to four out of eight circumstances in the same way that an autistic child diagnosed by specialists would, the player will be tagged as autistic as well. All participants identified as autistic by this test should be checked by a specialist for a final diagnosis. Conclusions: ASD lacks a simple medical test for diagnosis, necessitating observation and questioning by trained professionals, especially in children. Conventional diagnostic approaches are time-consuming and financially demanding, making them less accessible for many families. Given the critical importance of early ASD diagnosis and its impact on learning and development, we were able to create a tool that will help in the diagnosis process, which will lead to solving many problems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it