20.500.12556/RUL-153065 BASE: Brain Age Standardized Evaluation Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain age prediction, often falling within a 2–3 year range, is achieved predominantly through deep neural networks. However, comparing study results is difficult due to differences in datasets, evaluation methodologies and metrics. Addressing this, we introduce Brain Age Standardized Evaluation (BASE), which includes (i) a standardized T1w MRI dataset including multi-site, new unseen site, test-retest and longitudinal data, and an associated (ii) evaluation protocol, including repeated model training and upon based comprehensive set of performance metrics measuring accuracy, robustness, reproducibility and consistency aspects of brain age predictions, and (iii) statistical evaluation framework based on linear mixed-effects models for rigorous performance assessment and cross-comparison. To showcase BASE, we comprehensively evaluate four deep learning based brain age models, appraising their performance in scenarios that utilize multi-site, test-retest, unseen site, and longitudinal T1w brain MRI datasets. Ensuring full reproducibility and application in future studies, we have made all associated data information and code publicly accessible at https://github.com/AralRalud/BASE.git. brain age evaluation deep regression accuracy robustness reproducibility consistency UK biobank možganska starost vrednotenje globoka regresija točnost robustnost ponovljivost konsistentnost UK biobank true false true Angleški jezik Slovenski jezik Članek v reviji 2023-12-15 14:32:41 2023-12-15 14:32:43 2025-09-18 09:06:55 0000-00-00 00:00:00 2024 0 0 15 str. Vol. 285, art. 120469 Jan. 2024 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 616.8:004 1095-9572 10.1016/j.neuroimage.2023.120469 176687875 1-s2.0-S1053811923006183-main.pdf 1-s2.0-S1053811923006183-main.pdf 1 E083EEFDD27F354AFC74C1F2676B299A 21843fc7b6bfb787ce4f97f14f65da1c5336dc4394e3172fb81c8399336da3bb a7b003bd-9b4e-11ee-bebf-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=178867 https://www.sciencedirect.com/science/article/pii/S1053811923006183 1 6d53225a-9b4e-11ee-bebf-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=178866 Fakulteta za elektrotehniko 0 0 0