ArticleScientific reports2020
Identification of patients with and without minimal hepatic encephalopathy based on gray matter volumetry using a support vector machine learning algorithm.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Machine learning techniques in hepatic encephalopathy: a scoping review.BMC medical informatics and decision making · 2025Article
- Normative Modeling Reveals Age-Atypical Cortical Thickness Differences Between Hepatic Steatosis and Fibrosis in Non-Alcoholic Fatty Liver Disease.Brain and behavior · 2025Article
- Interpretable machine learning model for predicting covert hepatic encephalopathy in patients with cirrhosis: a multicenter study.Frontiers in medicine · 2025Article
- Minimal hepatic encephalopathy: Characteristics and comparison of the main diagnostic modalities.Clinical and experimental hepatology · 2024Review
- Changes in dynamic and static brain fluctuation distinguish minimal hepatic encephalopathy and cirrhosis patients and predict the severity of liver damage.Frontiers in neuroscience · 2023Article
- The Link between Gut Microbiota and Hepatic Encephalopathy.International journal of molecular sciences · 2022Review
- Psychometric methods for diagnosing and monitoring minimal hepatic encephalopathy -current validation level and practical use.Metabolic brain disease · 2022Review
- Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis.Journal of personalized medicine · 2021Review
- Reduced Cortical Complexity in Cirrhotic Patients with Minimal Hepatic Encephalopathy.Neural plasticity · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Minimal hepatic encephalopathy (MHE) is characterized by diffuse abnormalities in cerebral structure, such as reduced cortical thickness and altered brain parenchymal volume. This study tested the potential of gray matter (GM) volumetry to differentiate between cirrhotic patients with and without MHE using a support vector machine (SVM) learning method. High-resolution, T1-weighted magnetic resonance images were acquired from 24 cirrhotic patients with MHE and 29 cirrhotic patients without MHE (NHE). Voxel-based morphometry was conducted to evaluate the GM volume (GMV) for each subject. An SVM classifier was employed to explore the ability of the GMV measurement to diagnose MHE, and the leave-one-out cross-validation method was used to assess classification accuracy. The SVM algorithm based on GM volumetry achieved a classification accuracy of 83.02%, with a sensitivity of 83.33% and a specificity of 82.76%. The majority of the most discriminative GMVs were located in the bilateral frontal lobe, bilateral lentiform nucleus, bilateral thalamus, bilateral sensorimotor areas, bilateral visual regions, bilateral temporal lobe, bilateral cerebellum, left inferior parietal lobe, and right precuneus/posterior cingulate gyrus. Our results suggest that SVM analysis based on GM volumetry has the potential to help diagnose MHE in cirrhotic patients.
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