Exploratory Study Uses fNIRS and Machine Learning to Differentiate Autism in Young Children
No Comments on Exploratory Study Uses fNIRS and Machine Learning to Differentiate Autism in Young Children
A study explored using functional near-infrared spectroscopy (fNIRS) with machine learning to classify autism in children aged 2-6, finding modest accuracy and no significant diagnostic advantage beyond age and sex.
Call for Clarification on ‘Neuromimicnet’ Study Addressing Early Autism Screening
A letter published in the Journal of Multidisciplinary Healthcare calls for clarification of inconsistencies in the Neuromimicnet framework study for early autism screening.
NeuroCypher ASD: A New Machine Learning Tool to Support Early Autism Screening in Toddlers
Researchers have developed NeuroCypher ASD, a machine learning and knowledge graph-based framework to improve early screening and interpretation of Autism Spectrum Disorder risk in toddlers.
Emerging Research Highlights Potential Biomarkers for Autism Spectrum Disorder
Recent studies highlight potential biological markers that may aid in earlier and more accurate autism diagnosis, though research remains preliminary.