By employing the Cinemetrics method, the team successfully extracted micro-expressions from films featuring ASD patients and utilized an enhanced YOLOv8-SMART algorithm for precise detection.

Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information. JAMA Network Open , 2024; 7 (8): e2429229 DOI: 10.1001/jamanetworkopen.2024.29229 ...

The model can facilitate early detection of autism, which is important to provide the right support. "With an accuracy of almost 80% for children under the age of two, we hope that this will be a ...

Artificial intelligence, coupled with data from an iPad coloring game, could assist in early diagnosis of autism, a new USC study shows. "These results indicate the potential for an easy and novel ...

In this paper, deep Convolutional Neural Network (CNN) with Dwarf Mongoose ... optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the ...

A nurse practitioner who focuses on neonatal developmental follow-up and autism screening says there are hallmark ... Tara Calligan: Early detection of anything, when you hear advice from medical ...

Introduction: Can we apply graph representation learning algorithms to identify autism spectrum disorder (ASD) patients within a large brain imaging dataset? ASD is mainly identified by brain ...

By using the deep learning for ASD detection, it is predicted to be possible to speed up and enhance the accuracy of diagnosis. This paper gives a technical overview of how to use the CNN algorithm of ...

Their ultimate goal is to create an algorithm that exhibits equal or better performance in the early detection of autism in children when compared to traditional diagnostic methods, which require ...