Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918054617677824 |
|---|---|
| author | Copiaco, Abigail Ritz, Christian Himeur, Yassine Eapen, Valsamma Albanna, Ammar Mansoor, Wathiq |
| author_facet | Copiaco, Abigail Ritz, Christian Himeur, Yassine Eapen, Valsamma Albanna, Ammar Mansoor, Wathiq |
| contents | The prevalence of Autism Spectrum Disorder (ASD) has surged rapidly over the past decade, posing significant challenges in communication, behavior, and focus for affected individuals. Current diagnostic techniques, though effective, are time-intensive, leading to high social and economic costs. This work introduces an AI-powered assistive technology designed to streamline ASD diagnosis and management, enhancing convenience for individuals with ASD and efficiency for caregivers and therapists. The system integrates transfer learning with image transforms derived from eye gaze variables to diagnose ASD. This facilitates and opens opportunities for in-home periodical diagnosis, reducing stress for individuals and caregivers, while also preserving user privacy through the use of image transforms. The accessibility of the proposed method also offers opportunities for improved communication between guardians and therapists, ensuring regular updates on progress and evolving support needs. Overall, the approach proposed in this work ensures timely, accessible diagnosis while protecting the subjects' privacy, improving outcomes for individuals with ASD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis Copiaco, Abigail Ritz, Christian Himeur, Yassine Eapen, Valsamma Albanna, Ammar Mansoor, Wathiq Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning The prevalence of Autism Spectrum Disorder (ASD) has surged rapidly over the past decade, posing significant challenges in communication, behavior, and focus for affected individuals. Current diagnostic techniques, though effective, are time-intensive, leading to high social and economic costs. This work introduces an AI-powered assistive technology designed to streamline ASD diagnosis and management, enhancing convenience for individuals with ASD and efficiency for caregivers and therapists. The system integrates transfer learning with image transforms derived from eye gaze variables to diagnose ASD. This facilitates and opens opportunities for in-home periodical diagnosis, reducing stress for individuals and caregivers, while also preserving user privacy through the use of image transforms. The accessibility of the proposed method also offers opportunities for improved communication between guardians and therapists, ensuring regular updates on progress and evolving support needs. Overall, the approach proposed in this work ensures timely, accessible diagnosis while protecting the subjects' privacy, improving outcomes for individuals with ASD. |
| title | Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2506.09065 |