Innovative Hand-Grasp Analysis Aids in Simplifying Autism Spectrum Disorder Diagnosis

Early and accurate diagnosis of autism spectrum disorder (ASD) remains a significant challenge in clinical practice. Recent research from York University proposes that examining how individuals grasp objects could serve as an accessible, scalable diagnostic tool for ASD. The study, published in the journal Autism Research, utilized machine learning to analyze naturalistic hand movements, specifically finger motions during grasping, in both autistic and non-autistic young adults.

The research involved participants using their thumbs and index fingers, fitted with tracking markers, to perform tasks such as grasping, lifting, and replacing objects of varying sizes. Despite having similar IQ levels and being matched for age, the autistic group exhibited subtle yet distinct differences in their grasping motions compared to non-autistic counterparts. The machine learning models achieved approximately 85% accuracy in classifying autism based on these motor patterns, highlighting the potential for such methods to serve as diagnostic indicators.

This approach emphasizes the importance of motor control analysis, which has often been overlooked in traditional autism evaluations focused on social and communication behaviors. Since motor abnormalities can be observed early in childhood, assessing these subtle movements could facilitate earlier diagnosis and intervention, which are crucial for improved outcomes. Experts involved in the study, including Professor Batsheva Hadad, suggest that early detection through motor pattern analysis could lower the age of diagnosis, offering timely support.

The study's lead author, Associate Professor Erez Freud, explains that machine learning provides a powerful new tool to analyze movement data, paving the way for the development of more reliable and accessible diagnostic methods. In addition to social and communication challenges, motor abnormalities are recognized features of neurodevelopmental disorders like ASD. Focusing on naturalistic grasping behaviors allows for more realistic assessment settings, which could be implemented in clinical or portable screening tools in the future.

Ultimately, this research underscores the promising role of motor control analysis in identifying autism sooner and more accurately, opening new avenues for early intervention strategies that can significantly enhance quality of life for autistic individuals.

Source: https://medicalxpress.com/news/2025-05-individuals-grasp-simpler-diagnosis-autism