Machine learning on a smartphone-based CPT for ADHD prediction

The study provides a robust machine-learning model that is based on a large control group together with an ADHD cohort. The experiments demonstrated that ADHD can be assessed with high accuracy using CPTs on smartphones. Integrating face-tracking and motion sensor data with CPT features further enhanced performance, indicating that data from a smartphone device can surpass the accuracy of traditional computer-based ADHD assessments.

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Publication Details

Journal
Frontiers in Psychiatry
Published
2025-11-07
DOI
https://doi.org/10.3389/fpsyt.2025.1564351
Primary Topic
Attention Deficit Hyperactivity Disorder
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine learning on a smartphone-based CPT for ADHD prediction

Núria Casals, Simon Larsson, Mikkel Hansen
Frontiers in Psychiatry
Attention Deficit Hyperactivity Disorder
article

Machine learning on a smartphone-based CPT for ADHD prediction

Núria Casals, Simon Larsson, Mikkel Hansen
article en

Abstract

The study provides a robust machine-learning model that is based on a large control group together with an ADHD cohort. The experiments demonstrated that ADHD can be assessed with high accuracy using CPTs on smartphones. Integrating face-tracking and motion sensor data with CPT features further enhanced performance, indicating that data from a smartphone device can surpass the accuracy of traditional computer-based ADHD assessments.

Frontiers in PsychiatryVol. 16
Dextech Medical (Sweden) (SE), Mabtech (Sweden) (SE)
Openalex Percentile: Top 61%
Attention Deficit Hyperactivity Disorder
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Machine learning on a smartphone-based CPT for ADHD prediction — Núria Casals, Simon Larsson, et al. · Frontiers in Psychiatry (2025) | TGRS Research Map | TGRS