ArticleAbdominal radiology (New York)2025
Diagnosis of moderate-to-severe hepatic steatosis using deep learning-based automated attenuation measurements on contrast-enhanced CT.
Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- CT Attenuation Thresholds for Grading Hepatic Steatosis in Adults with Overweight or Obesity and Metabolic Dysfunction-Associated Steatotic Liver Disease.Diagnostics (Basel, Switzerland) · 2026Article
- 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
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
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8 authors.
Funding
Abstract
purposeTo evaluate the utility of deep learning-based automated attenuation measurements on contrast-enhanced CT (CECT) for diagnosing moderate-to-severe hepatic steatosis (HS), using histology as reference standard.
methodsThis retrospective study included 3,620 liver donors (2,393 men and 1,227 women; mean age, 31.7 ± 9.4 years), divided into the development (n = 2,714) and test (n = 906) cohorts. Attenuation values of the liver and spleen on CECT were measured both manually and using a deep learning algorithm (before and after radiologists' correction of segmentation errors). Performance of: (1) liver attenuation and (2) liver-spleen attenuation difference for diagnosing moderate-to-severe HS (> 33%) was assessed using the area under the receiver operating characteristic curve (AUC). Three different criteria targeting 95% sensitivity, 95% specificity, and the maximum Youden's index, respectively, for diagnosing moderate-to-severe HS, were developed and validated.
resultsThe performance of deep learning-based measurements did not differ significantly, with or without radiologists' corrections (p = 0.13). Liver-spleen attenuation difference outperformed liver attenuation alone in diagnosing moderate-to-severe HS in both deep learning-based (AUC, 0.868 vs. 0.821; p = 0.001) and manual (AUC, 0.871 vs. 0.823; p = 0.001) measurements. In the test cohort, the criterion targeting 95% sensitivity for diagnosing moderate-to-severe HS (liver-spleen attenuation difference ≤ 2.8 HU) yielded 92.0% (69/75) sensitivity and 48.5% (403/831) specificity. The criterion targeting 95% specificity (liver-spleen attenuation difference ≤ -18.8 HU) yielded 53.3% (40/75) sensitivity and 95.7% (795/831) specificity. The criterion targeting the maximum Youden's index (liver-spleen attenuation difference ≤ -8.2 HU) yielded 82.7% (62/75) sensitivity and 80.7% (671/831) specificity.
conclusionDeep learning-based automated measurements of liver and spleen attenuation on CECT can be used reliably to detect moderate-to-severe HS.
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