New diagnostic approach uses motor markers in autism
A groundbreaking study published in the Journal Autism Research on 5 May 2025, you have identified a potentially simpler approach to diagnose the diagnosis of autism spectrum spread by diagnosing hand movements during the grasping of Evenhay.
The international research team, led by assistant professor Erez Freud from the Psychology department of York University and the Center for Vision Research, used machine learning to analyze naturalistic finger movements during grass glazing by autistic and non-autistic young adults.
“Our models are to classify autism with approximately 85 percent accuracy, which suggests that this approach may offer easier, scalable aids for diagnosis,” says Freud.
Abnormalitis motor offers early diagnostic opportunity
Autism spectrum disorder Affirmers about one in 50 Canadian children and is usually diagnosed by behavioral assessments that focus on challenges on social communication and repetitive behavior. These characteristic markers offs, however, seem later late in revealopment relationship.
The research team notes that motorcycle abnormalities, which are widely documed in autism, often manifest themselves in early childhood and possibly offer previous diagnostic signals – the submission of not yet widespread in clinical practice.
“The most important behavioral markers for diagnosis are aimed at people with ReativeLey Laat and the motor markers who can be caught early in childhood, can therefore lower the age of diagnosis,” explains Professor Batsheva Hadad of the University of Haifa, to an important employee in the study.
Study method aimed at natural movements
The recruited researchers 31 autistic and 28 non-autistic young adults with normal IQ scores. Participants were asked to perform with simple grabs- with the help of their thumbs and index fingers to grab, lift and replace blocks of different sizes, while following markers who are set to their fingers, accurate movement data.
By concentrating on young adults instead of children, the study is that any differences perceive Coul does not attribute to developmental trade, but renowned reflected financed financed differences in motor control.
The research team used five different classifications for machine learning to analyze the data, whereby the accuracy of the consistency classification above 84% was reached in all models. When investigating the area under the Curve (AUC)-A size for classification-relevant appropriate, the scores above 0.95 for the subject-wise analysis and more than 0.85 in the test analysis.
Possible classification with minimal functions
A private promising finding was that the classifications maintained high accuracy, even when using a reduced set of functions. With only eight carefully selected, minimally correlated functions that include multiple domains experimental, including state, timing information, speed data and location information information-the classifications 82-86% accuracy achieved.
“Bese findings suggest that subtle engine control differences can be effectively recorded, making a promising approach for accessible and reliable diagnostic aids for autism,” pay attention to the authors in their paper.
Implications for earlier diagnosis and intervention
The authors emphasize the potential clinical significance of their work in the conclusion of the paper: “The current study provides strong evidence that understanding movements is strong diagnostic for autism, and that ML techniques can be used to improve the robustness of such diagnosis. diagnostic aids for autodiagnostools for cars. “
This approach could supplement existing diagnostic methods and possibly make it possible for a previous intervention, which is known to improve the results for autistic individuals. The refectchers suggest that further studies should investigate whether comparable kinematic markers can be observed among the younger population, especially in the early childhood when the visu -motor system still develops.
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