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Abstract #3610

Automatic whole-brain segmentation analysis for diagnosing Sepsis-Associated Encephalopathy

Dong Liu1 and Weiyin Vivian Liu2
1Department of Radiology, Tongji Hosptial, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, 2MR Research, GE Healthcare, Beijing, China

Synopsis

Keywords: Neuroinflammation, Neuro

Motivation: Sepsis-associated encephalopathy (SAE) is a severe neurological complication of sepsis, and its early diagnosis is difficult due to the lack of specific biomarkers.

Goal(s): This study aims to assess the utility of FreeSurfer-based MRI brain segmentation to improve the diagnosis precision of SAE.

Approach: 3D T1-weighted MRI scans on 22 SAE patients and 35 matched healthy controls. 546 brain regions were analyzed to detect structural changes associated with SAE.

Results: Significant structural differences were observed in regions such as the hippocampus, thalamus, corpus callosum. Among the three diagnostic models developed, Model 3 exhibited the best predictive performance, with an AUC of 0.971.

Impact: Whole brain segmentation using FreeSurfer, combined with artificial intelligence, offers a promising non-invasive method for diagnosing SAE. This approach could facilitate earlier detection and intervention, ultimately improving patient outcomes.

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