The research aims to augment current screening tools which are effective but can be costly, time-consuming and focus on only one condition at a time.
It could eventually be a salve for the shortage of speech-language pathologists and
Venu Govindaraju, PhD, SUNY Distinguished Professor in the Department of Computer Science and Engineering at UB, is co-author of the study.
The researcher said: “Catching these neurodevelopmental disorders early is critically important to ensuring that children receive the help they need before it negatively impacts their learning and socio-emotional development.
“Our ultimate goal is to streamline and improve early screening for dyslexia and dysgraphia, and make these tools more widely available, especially in underserved areas.”
Decades ago, Govindaraju and colleagues did groundbreaking work employing machine learning, natural language processing and other forms of AI to analyse handwriting, an advancement the US Postal Service and other organisations still use to automate the sorting of mail.
The new study proposes a similar framework and methodologies to identify spelling issues, poor letter formation, writing organisation problems and other indicators of dyslexia and dysgraphia.
It aims to build upon prior research, which has focused more on using AI to detect dysgraphia (the less common of the two conditions) because it causes physical differences that are easily observable in a child’s handwriting.
Dyslexia is more difficult to spot this way because it focuses more on reading and speech, though certain behaviors like spelling offers clues.
The study also notes there is a shortage of handwriting examples from children to train AI models with.
To address these challenges, a team of UB computer scientists led by Govindaraju gathered insight from teachers, speech-language pathologists and occupational therapists to help ensure the AI models they’re developing are viable in the classroom and other settings.
Sahana Rangasrinivasan is a PhD student in UB’s Department of Computer Science and Engineering and co-author of the study..
Rangasrinivasan said: “It is critically important to examine these issues, and build AI-enhanced tools, from the end users’ standpoint.”
The team collected paper and tablet writing samples from kindergarten through 5th grade students at an elementary school in Reno.
This part of the study was approved by an ethics board, and the data was anonymised to protect student privacy.
They will use this data to further validate the DDBIC tool, which focuses on 17 behavioral cues that occur before, during and after writing; train AI models to complete the DDBIC screening process; and compare how effective the models are compared to people administering the test.
