ArticlePediatric radiology2021
DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults.
Article in Pediatric radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 16 citations in OpenAlex.
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Development and validation of a deep learning model for liver shear stiffness regression using abdominal multiparametric MRI across multiple sites and vendors.European radiology · 2026Article
- Prediction of Fontan failure and correlates of Fontan-associated liver disease severity using machine learning and radiomic features from multi-parametric abdominal MRI.Pediatric radiology · 2026Article
- Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI.European radiology · 2025Article
- ENRICHing medical imaging training sets enables more efficient machine learning.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- The current and future roles of artificial intelligence in pediatric radiology.Pediatric radiology · 2022Review
- A primer on texture analysis in abdominal radiology.Abdominal radiology (New York) · 2022Review
- Deep learning augments liver stiffness classification in children.Pediatric radiology · 2021Article
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8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAlthough MR elastography allows for quantitative evaluation of liver stiffness to assess chronic liver diseases, it has associated drawbacks related to additional scanning time, patient discomfort, and added costs.
objectiveTo develop a machine learning model that can categorically classify the severity of liver stiffness using both anatomical T2-weighted MRI and clinical data for children and young adults with known or suspected pediatric chronic liver diseases. MATERIALS AND
methodsWe included 273 subjects with known or suspected chronic liver disease. We extracted data including axial T2-weighted fast spin-echo fat-suppressed images, clinical data (e.g., demographic/anthropomorphic data, particular medical diagnoses, laboratory values) and MR elastography liver stiffness measurements. We propose DeepLiverNet (a deep transfer learning model) to classify patients into one of two groups: no/mild liver stiffening (<3 kPa) or moderate/severe liver stiffening (≥3 kPa). We conducted internal cross-validation using 178 subjects, and external validation using an independent cohort of 95 subjects. We assessed diagnostic performance using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AuROC).
resultsIn the internal cross-validation experiment, the combination of clinical and imaging data produced the best performance (AuROC=0.86) compared to clinical (AuROC=0.83) or imaging (AuROC=0.80) data alone. Using both clinical and imaging data, the DeepLiverNet correctly classified patients with accuracy of 88.0%, sensitivity of 74.3% and specificity of 94.6%. In our external validation experiment, this same deep learning model achieved an accuracy of 80.0%, sensitivity of 61.1%, specificity of 91.5% and AuROC of 0.79.
conclusionA deep learning model that incorporates clinical data and anatomical T2-weighted MR images might provide a means of risk-stratifying liver stiffness and directing the use of MR elastography.
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