Machine learning on a smartphone-based CPT for ADHD prediction
Bibliographic Data
| ID | 15525478 |
|---|---|
| Authors | Nuria Casals (0000-0002-6719-4300, Dextech Medical (Sweden), corresponding author), Simon Larsson (0000-0002-2589-7964, Dextech Medical (Sweden), corresponding author), Mikkel Hansen (Dextech Medical (Sweden)) |
| Year | 2025 |
| Volume | 16 |
| Pages | 1564351-1564351 |
| Publication date | 2025-11-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2025.1564351 |
| PMID | 41280456 |
| OpenAlex | W4415983600 |
| Language | EN |
| References cited | 3 |
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
Control (management · Motion (physics · Motion sensors · Random forest · Attention Deficit Hyperactivity Disorder · EEG and Brain-Computer Interfaces · Neural and Behavioral Psychology Studies
| Highly cited | No |
|---|