{"id":88722,"date":"2024-10-02T17:59:14","date_gmt":"2024-10-02T15:59:14","guid":{"rendered":"https:\/\/intercoaching.fr\/?p=88722"},"modified":"2024-10-02T19:36:41","modified_gmt":"2024-10-02T17:36:41","slug":"how-an-ai-detects-autism-with-100-reliability-by-observing-the-retina","status":"publish","type":"post","link":"https:\/\/intercoaching.fr\/en\/how-an-ai-detects-autism-with-100-reliability-by-observing-the-retina\/","title":{"rendered":"How an AI detects autism with 100% reliability by observing the retina"},"content":{"rendered":"
Screening for autism using artificial intelligence through retinal examination<\/h2>\n\n\n
Rapid and accurate detection of autism in young people represents a major challenge that could be addressed thanks to advances in artificial intelligence (AI). AI today offers a revolutionary perspective: the analysis of retinal images with almost perfect reliability assurance. This is what research conducted in South Korea at Yonsei University demonstrates, suggesting a future where screening could be carried out non-invasively, with rapid and accurate results.<\/p>\n\n\n
A window on the brain: the retina<\/h2>\n\n\n\n\n<\/figure>\n\n\n
Considered an extension of the central nervous system, the retina offers a unique reflection of our neurological state. In children suffering from autism spectrum disorder (ASD), scientists have discovered unusual retinal features. These differences are a manifestation of the neurological alterations inherent in autism, and they can be identified using precise photographs of the retina which are then scrutinized by a sophisticated AI system.<\/p>\n\n\n
Learning to see: teaching AI<\/h2>\n\n\n\n\n<\/figure>\n\n\n
Deep learning is at the heart of this method. The AI \u200b\u200balgorithm is trained using a large collection of retinal images from both children with ASD and those without the disorder, all matched by age and gender. Researchers even took into account the severity of ASD symptoms, using scales such as the ADOS-2 and SRS-2 to refine the sensitivity of their approach. After training on 85% of the dataset and validation on the remaining 15%, the AI \u200b\u200bproved to be exemplary accurate.<\/p>\n\n\n\n
AI: a tool of the future for assessing ASD<\/h2>\n\n\n\n\n<\/figure>\n\n\n
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Promise of easier access to early and objective screening, essential for effective interventions.<\/li>\n\n\n
A reduction in health disparities thanks to technology that can be used even in regions lacking specialized resources.<\/li>\n\n\n
Avoids possible subjective biases during traditional behavioral assessments.<\/li>\n\n<\/ul>\n\n\n
Current limits and prospects<\/h2>\n\n\n
The technology is still in its infancy and requires further validation. Additional studies are needed to confirm its effectiveness on a large scale, test its applicability across age groups, genders, and various cultural and social contexts. In addition, variations in retinal development pose questions for the accuracy of diagnosis in very young children.<\/p>\n\n\n
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