ArticleFrontiers in human neuroscience2020
A Machine-Based Prediction Model of ADHD Using CPT Data.
Article in Frontiers in human neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 74 citations in OpenAlex.
- Serious gaming and eye-tracking for the screening, monitoring, and diagnosis of neurodevelopmental disorders in children: a systematic literature review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Artificial intelligence in ADHD: a global perspective on research hotspots, trends and clinical applications.Frontiers in human neuroscience · 2025Pooled it
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Artificial Intelligence, Machine Learning, and Deep Learning Approaches for Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Assessing Executive Function in Patients With Temporal Lobe Epilepsy Through a Game-Based Assessment Task.Behavioural neurology · 2026Article
- Predicting ADHD in Children and Adolescents With Artificial Intelligence: A Scoping Review of Common Models.Health science reports · 2025Article
- Exploring voice as a digital phenotype in adults with ADHD.Scientific reports · 2025Article
- Retinal fundus imaging as biomarker for ADHD using machine learning for screening and visual attention stratification.NPJ digital medicine · 2025Article
- Objective Assessment of Attention Deficit Hyperactivity Disorder with QbMobile: A Smartphone Application for Clinical Use.Clinical practice and epidemiology in mental health : CP & EMH · 2025Article
- Machine learning on a smartphone-based CPT for ADHD prediction.Frontiers in psychiatry · 2025Article
- Prediction of attention deficit hyperactivity disorder using the comprehensive attention test: a large-scale machine learning approach.Frontiers in psychiatry · 2025Article
- Unlocking the potential of wearable technology: Fitbit-derived measures for predicting ADHD in adolescents.Frontiers in child and adolescent psychiatry · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.Translational psychiatry · 2024Article
- The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder.Journal of neurodevelopmental disorders · 2024Article
- Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024Article
- A Nomogram for Predicting ADHD and ASD in Child and Adolescent Mental Health Services (CAMHS).Journal of clinical medicine · 2024Article
- Article
- Inclusion of a computerized test in ADHD diagnosis process can improve trust in the specialists' decision and elevate adherence levels.Scientific reports · 2024Observational
- BOLD Activation During the Application of MOXO-CPT in School Patients With and Without Attention Deficit Hyperactivity Disorder.Journal of attention disorders · 2024Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learning-based model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6-12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.