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.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
- Clinical Epidemiology and EBM Unit, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- MR Research, GE Healthcare, Beijing 100176, China.
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- Precision and Intelligence Medical Imaging Lab, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Abstract
<b>Introduction:</b> 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). <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> 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.