To provide a focused overview of neuroimaging for Autism Spectrum Disorder (ASD) diagnosis, highlighting recent advancements in multimodal neuroimaging techniques.
Approach:
Neuroimaging Modalities: The review covers four primary neuroimaging modalities: functional MRI (fMRI), structural MRI (sMRI), Electroencephalography (EEG), and multimodal approaches.
Machine Learning Analysis: It includes an in-depth analysis of key diagnostic features used in machine learning and deep learning-based ASD classification.
Comparative Summary: A comparative summary of diagnostic performance, focusing on accuracy (Acc) and Area Under the Curve (AUC) values, is presented.
Dataset Overview: An organized overview of the most frequently used neuroimaging datasets for ASD diagnosis is provided.
Future Directions: The article identifies potential concerns and future directions to improve the robustness, generalizability, and clinical applicability of computational approaches.
Key Findings:
A comprehensive analysis of 107 key studies from 2015 to 2021 is included.
Interpretation:
The review highlights the importance of integrating machine learning with neuroimaging techniques to improve ASD diagnosis.
Limitations:
Many existing studies focus on individual neuroimaging modalities rather than multimodal approaches.
Previous reviews may be outdated or narrowly scoped.
Conclusion:
The review aims to fill gaps in the literature by providing a current perspective on ASD diagnosis using neuroimaging.
A VHA study across 11 vendors finds AI-generated primary care notes score lower than clinician-written notes, with the largest deficits in thoroughness, organization, and usefulness
