RT info:eu-repo/semantics/article
T1 Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups
A1 Amado Caballero, Patricia
A1 Casaseca de la Higuera, Juan Pablo
A1 Alberola López, Susana
A1 Andrés de Llano, Jesús María
A1 López Villalobos, José Antonio
A1 Alberola López, Carlos
K1 Diagnóstico
K1 Hiperactividad
K1 ADHD
K1 Actigraphy
K1 Deep learning
K1 TDAH
K1 Actigrafía
K1 Aprendizaje profundo
K1 3205.07 Neurología
K1 32 Ciencias Médicas
AB 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.
PB Elsevier
SN 0933-3657
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/61199
UL https://uvadoc.uva.es/handle/10324/61199
LA eng
NO Artificial Intelligence in Medicine, 2023, vol. 143, 102630
NO Producción Científica
DS UVaDOC
RD 04-dic-2024