2026-04-17T02:47:53Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4462552026-02-07T07:26:12Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Romagosa Pérez, Júlia author Mata Miquel, Cristian author Benítez, Raúl author Valls Esteve, Arnau author Bernaus, Sandra author Ibnoulkhatib, Maria author Stephan-Otto, Christian author Munuera del Cerro, Josep author 2025-10-20 Purpose The study investigates the usefulness of Convolutional Neural Networks (CNNs) in accurately detecting arteriovenous malformations in pediatric medical imaging, particularly using arterial spin labeling sequences. It also aims to offer diagnostic explanations comparable to expert analysis. Methods The research analyzed three different CNN architectures to determine their performance in detecting arteriovenous malformations. The study focused on evaluating the relationship between model complexity and performance increase, using data to assess the accuracy and diagnostic usefulness of each model. Results The findings indicated a nonlinear link between model complexity and performance. Sur- prisingly, more complex models frequently produced poor results and diagnostically useless answers. The simplest CNN models achieved the highest accuracy rate (90%), demonstrating the effectiveness of minimal complexity in model construction. Heat maps showed a strong association with the real locations of irregularities, indicating that the models were interpretable. Conclusion The study highlights the usefulness of CNNs in medical diagnostics, emphasizing the importance of model simplicity and interpretability in clinical applications. It suggests a need for balancing technical sophistication with clinical value and presents options for future research into refining CNN structures for increased diagnostic precision in various medical imaging modalities. Peer Reviewed Postprint (author's final draft) Àrees temàtiques de la UPC::Enginyeria biomèdica Medical informatics Medicina--Informàtica GradCAM as an explicability method to evaluate the performance of deep learning models in classifying pediatric arteriovenous malformations (AVM) in arterial spin labeling sequences (ASL)