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oai:uvadoc.uva.es:10324/611992025-02-20T13:52:28Zcom_10324_55583com_10324_22818com_10324_894col_10324_55585
Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups Amado Caballero, Patricia Casaseca de la Higuera, Juan Pablo Alberola López, Susana Andrés De Llano, Jesús María López Villalobos, José Antonio Alberola López, Carlos Diagnóstico Hiperactividad Producción Científica Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can be challenging due to the reliance on subjective questionnaires in clinical assessment. Fortunately, recent advancements in artificial intelligence (AI) have shown promise in providing objective diagnoses through the analysis of medical images or activity recordings. These AI-based techniques have demonstrated accurate ADHD diagnosis; however, the growing complexity of deep learning models has introduced a lack of interpretability. These models often function as black boxes, unable to offer meaningful insights into the data patterns that characterize ADHD. 2023-08-29 2023-08-29 2023 info:eu-repo/semantics/article Artificial Intelligence in Medicine, 2023, vol. 143, 102630 0933-3657 https://uvadoc.uva.es/handle/10324/61199 10.1016/j.artmed.2023.102630 102630 Artificial Intelligence in Medicine 143 eng https://www.sciencedirect.com/science/article/pii/S0933365723001446?via%3Dihub info:eu-repo/grantAgreement/EC/H2020/101008297 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2023 The Authors Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier