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.
6 affiliations
- 1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- 2State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
- 3Clinical Epidemiology and EBM Unit, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- 4MR Research, GE Healthcare, Beijing 100176, China.
- 5Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- 6Precision and Intelligence Medical Imaging Lab, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
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.
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BibTeX
@article{yang2026deep,
title = {{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.}},
author = {Wenbo Yang and Lijun Song and Hang Lei and Shiqing Wang and Xintian Ren and Yanfang Tan and Boyan Xu and Zhenghan Yang and Min Li and Weikang Guo and Anfu Zhou and Zhenchang Wang and Hao Wang},
journal = {Cyborg and bionic systems (Washington, D.C.)},
year = {2026},
month = nov,
doi = {10.34133/cbsystems.0733},
url = {https://europepmc.org/article/MED/42996771},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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.
AU - Wenbo Yang
AU - Lijun Song
AU - Hang Lei
AU - Shiqing Wang
AU - Xintian Ren
AU - Yanfang Tan
AU - Boyan Xu
AU - Zhenghan Yang
AU - Min Li
AU - Weikang Guo
AU - Anfu Zhou
AU - Zhenchang Wang
AU - Hao Wang
PY - 2026
DA - 2026/11/06
JO - Cyborg and bionic systems (Washington, D.C.)
DO - 10.34133/cbsystems.0733
UR - https://europepmc.org/article/MED/42996771
AB - 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.
SN - 2097-1087
SN - 2692-7632
ER -