AI-driven approaches for dysgraphia diagnosis using online and offline handwriting data: A comprehensive scoping review.

ABSTRACT: Dysgraphia, a neurodevelopmental disorder impacting writing abilities, is often overlooked or misdiagnosed, affecting academic and daily life activities. It affects both children, especially school-aged, and adults, potentially stemming from brain trauma or neurological diseases. Early diagnosis is crucial for effective management and intervention. With advancements in artificial intelligence (AI), there is potential to improve early diagnosis and intervention through automated detection methods. This scoping review aims to explore available studies employing AI-based models for dysgraphia diagnosis and identify the highest performance models and their challenges to suggest future improvements. Comprehensive searches were conducted across PubMed, Scopus, Web of Science, IEEE Xplore, and Sp

SUBMITTER: Fallah A 

PROVIDER: S-EPMC12755808 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature