In a significant leap forward for early autism detection, researchers have developed an artificial intelligence (AI) system capable of diagnosing the condition with 98.5% accuracy in children under two years old. Employing diffusion tensor magnetic resonance imaging (DT-MRI), this groundbreaking system scrutinizes the brain’s neural connections to identify patterns associated with autism.

Unveiled at the annual meeting of the Radiological Society of North America (RSNA), the system highlights its ability to analyze and categorize brain tissue images obtained through DT-MRI, a specialized technique that tracks the movement of water along the brain’s white matter tracts. The AI is trained to detect deviations in these connections indicative of autism by comparing image marker patterns in the brains of children with autism to those with typical development.

Developed by a multidisciplinary team led by Mohamed Khudri, B.Sc., a visiting researcher at the University of Louisville, Kentucky, the system promises to enhance early detection and treatment of autism, addressing current diagnostic delays due to limited testing resources. “Our approach is a novel advancement enabling early autism detection in infants under two years,” Khudri stated, emphasizing the importance of early intervention for achieving better outcomes, including the potential for individuals with autism to gain greater independence and higher IQs.

The analysis was applied to the DT-MRI brain scans of 226 children, showing 97% sensitivity, 98% specificity, and an overall accuracy of 98.5% in identifying children with autism. This approach not only promises to streamline precise autism management but also significantly reduce the time and costs associated with assessment and treatment.

The research team is actively working to commercialize the software and secure FDA approval, hoping this AI system can soon be implemented in clinical settings. This would provide healthcare professionals with a powerful tool for early autism detection, contributing to improved quality of life for affected children and their families.

This innovation comes at a crucial time, as according to the CDC’s 2023 Community Report on Autism, less than half of children with autism spectrum disorder received a developmental evaluation by three years of age, and 30% of children meeting autism spectrum disorder criteria had not received a formal diagnosis by eight years of age. The implementation of this AI system could mark a turning point in the fight against autism, offering hope to many families worldwide.

Conclusion:

The innovative application of an artificial intelligence (AI) system achieving 98.5% diagnostic accuracy in the early detection of autism in children under two years old represents a revolutionary advancement in the fields of medicine and technology. This system, based on the analysis of diffusion tensor magnetic resonance imaging (DT-MRI), promises not only to speed up and refine the diagnosis of autism but also to pave the way for more effective early interventions, significantly improving the developmental prospects and quality of life for affected children. This achievement highlights the transformative potential of AI in enhancing healthcare, marking a milestone in the fight against autism and reinforcing the importance of early detection and personalized treatment adaptation.

You also can visit:

• • SciTechDaily: Pioneering AI Technology Diagnoses Autism in Children Under Two With 98.5% Accuracy.

• • Health Imaging: AI able to identify autism in children with 98.5% accuracy.