ArticleScientific reports2022
Deep learning supports the differentiation of alcoholic and other-than-alcoholic cirrhosis based on MRI.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 31 citations in OpenAlex.
- An Attention-Residual Hybrid CNN for CT-Based Multiclass Classification of Alcohol-Related Liver Disease: Differential Diagnosis Against HBV-Related Cirrhosis.Diagnostics (Basel, Switzerland) · 2026Article
- Analysis of fetal MRI data reveals no effect on liver maturation following prenatal alcohol exposure.European radiology · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study.Journal of imaging informatics in medicine · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning.Diagnostics (Basel, Switzerland) · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Transformer-based structuring of free-text radiology report databases.European radiology · 2023Article
- Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy.Insights into imaging · 2023Article
- Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.Cancers · 2022Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although CT and MRI are standard procedures in cirrhosis diagnosis, differentiation of etiology based on imaging is not established. This proof-of-concept study explores the potential of deep learning (DL) to support imaging-based differentiation of the etiology of liver cirrhosis. This retrospective, monocentric study included 465 patients with confirmed diagnosis of (a) alcoholic (n = 221) and (b) other-than-alcoholic (n = 244) cirrhosis. Standard T2-weighted single-slice images at the caudate lobe level were randomly split for training with fivefold cross-validation (85%) and testing (15%), balanced for (a) and (b). After automated upstream liver segmentation, two different ImageNet pre-trained convolutional neural network (CNN) architectures (ResNet50, DenseNet121) were evaluated for classification of alcohol-related versus non-alcohol-related cirrhosis. The highest classification performance on test data was observed for ResNet50 with unfrozen pre-trained parameters, yielding an area under the receiver operating characteristic curve of 0.82 (95% confidence interval (CI) 0.71-0.91) and an accuracy of 0.75 (95% CI 0.64-0.85). An ensemble of both models did not lead to significant improvement in classification performance. This proof-of-principle study shows that deep-learning classifiers have the potential to aid in discriminating liver cirrhosis etiology based on standard MRI.
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