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Related Experiment Video

Updated: May 17, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Automatic choroid plexus assessment in SLE: a deep learning-enabled study.

Jun-Qi Chang1, Jia-Cheng Hao2, Xiao-Di Zhang3

  • 1Department of Radiology, Tianjin First Center Hospital, Tianjin, China.

Neuroradiology
|May 15, 2026
PubMed
Summary

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This summary is machine-generated.

Enlarged choroid plexus volume is a key biomarker and mediator of processing speed and attention deficits in SLE patients. A deep learning model accurately quantifies this, aiding in risk stratification for SLE-PSAD.

Area of Science:

  • Neuroimaging and Artificial Intelligence
  • Systemic Lupus Erythematosus (SLE) Pathophysiology
  • Cognitive Neuroscience

Background:

  • Systemic Lupus Erythematosus (SLE) can affect cognitive function, specifically processing speed and attention deficits (PSAD), even without major neuropsychiatric syndromes.
  • The choroid plexus (ChP) role in SLE-related cognitive dysfunction is not fully understood.
  • Accurate quantification of ChP volume is crucial for investigating its association with systemic inflammation and cognitive impairments.

Purpose of the Study:

  • To develop and validate a deep learning model (Swin-UNETR) for automated choroid plexus segmentation.
  • To investigate the relationship between ChP volume, systemic inflammation, and PSAD in SLE patients.
  • To identify biomarkers for predicting PSAD risk in SLE.

Main Methods:

Keywords:
Choroid plexusDeep learningMagnetic resonance imagingProcessing speed and attention deficitsSystemic lupus erythematosus

Related Experiment Videos

Last Updated: May 17, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

  • A multicenter retrospective study included 137 SLE patients and 159 healthy controls (HCs).
  • The Swin-UNETR model was trained for ChP segmentation on 3D T1-weighted MR images.
  • Correlation, mediation, and LASSO regression analyses were performed to assess relationships between ChP volume, systemic inflammation index (SII), complement component 3 (C3), and PSAD.

Main Results:

  • Swin-UNETR demonstrated high accuracy in ChP segmentation (median DSC=0.89 internal, 0.82 external).
  • SLE patients with PSAD exhibited significantly greater ChP volume compared to SLE patients without PSAD and HCs.
  • Increased ChP volume, elevated SII, and low C3 were identified as independent risk factors for PSAD, with ChP volume mediating the SII-PSAD relationship.

Conclusions:

  • Enlarged ChP volume serves as a significant biomarker and mediator for PSAD in SLE patients.
  • The Swin-UNETR deep learning model provides accurate ChP quantification.
  • A three-biomarker panel (ChP volume, C3, SII) offers a practical approach for SLE-PSAD risk stratification.