Work

Deep Learning to Identify Cognitive Impairment Across the Full Spectrum of Chronic Kidney Disease: Stage- and Cognition-Informed Feature Selection with a Dual-Branch Feature-Gated Network

Year: 2026

Type: article

Abstract: Introduction: Cognitive impairment (CI) is a frequent and clinically relevant complication of chronic kidney disease (CKD), yet the neuroimaging features associated with CI remain incompletely characterized. This study developed a stage- and cognition-informed neuroimaging feature-selection framework and integrated the selected features into a Dual-Branch Feature-Gated Network (DBFG-Net) for identifying CKD with CI (CKD-CI). Methods: We enrolled a discovery cohort of 342 patients with CKD, including 177 with CI and 165 without CI, and 58 age- and sex-matched healthy controls. Multimodal magnetic resonance imaging features were extracted from T1-weighted imaging, arterial spin labeling, and quantitative susceptibility mapping. Stage-related imaging patterns were first characterized across CKD stages 1 to 5, followed by cognition-related comparisons between CKD-CI and CKD without CI. Features showing both stage-related linear trends and cognition-related discrimination were selected for DBFG-Net training and tested in temporal and multicenter external validation cohorts. Results: Structural morphology and tissue susceptibility showed significant stage-related linear trends across CKD stages, whereas cerebral blood flow showed no significant stage-related linear trend. Consistently, discriminative features for CKD-CI were confined to structural and susceptibility measures. DBFG-Net achieved area-under-the-receiver-operating-characteristic-curve values of 0.942 in the discovery cohort and 0.902 and 0.926 in the 2 external validation cohorts. Conclusion: The stage- and cognition-informed framework identified structural and susceptibility features relevant to CKD-CI. DBFG-Net demonstrated robust performance for CKD-CI identification across the discovery and external validation cohorts.

Source: Cyborg and Bionic Systems

DOI: 10.34133/cbsystems.0733