ArticleLiver international : official journal of the International Association for the Study of the Liver2026
Identification of Hepatic Fibrosis and Steatosis via A Point-of-Care Transient Elastography System With Integrated AI.
Article in Liver international : official journal of the International Association for the Study of the Liver, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Expanding the Horizon: A Roadmap for Artificial Intelligence Integration in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Identification of Hepatic Fibrosis and Steatosis via A Point-of-Care Transient Elastography System With Integrated AI.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
BACKGROUND &
aimsTransient elastography (TE) is routinely undertaken for non-invasive assessment of liver fibrosis and steatosis, but is limited by its bulky design, inadequate imaging guidance and conventional algorithmic framework. Thus, we report a real-time B-mode image-guided, artificial intelligence-assisted, point-of-care TE (AI-POC-TE) system, providing simultaneous liver stiffness measurement (LSM) and a novel multi-domain attenuation parameter (MAP) for fat quantification. We aimed to determine the accuracy of LSM and MAP in diagnosing histology-confirmed fibrosis and steatosis in patients with chronic liver disease. Exploratory analyses assessed the minimum number of measurements required.
methodsThis prospective study included 138 patients who underwent liver biopsy and AI-POC-TE simultaneously, and diagnostic performance was evaluated by area under the receiver operating characteristic curve (AUROC). Another larger cohort of 1455 patients was examined to benchmark AI-POC-TE against conventional TE (Fibroscan).
resultsLSM by AI-POC-TE identified patients with fibrosis with AUROCs of 0.79 for ≥F2, 0.79 for ≥F3, 0.97 for F4. Corresponding Youden's cut-offs were 8.2, 9.1 and 14.4 kPa. MAP detected steatosis of ≥ S1, ≥ S2, S3 with AUROCs of 0.92, 0.70, 0.76 and Youden's cut-offs were 244, 278 and 294 dB/m, respectively. Among 1455 patients using both TE techniques, liver stiffness was highly correlated (r = 0.86) and MAP also correlated well with CAP (r = 0.80). Fewer than 10 measurements suffice to maintain accuracy; four measurements were statistically non-inferior to the standard 10, supporting a streamlined protocol.
conclusionWe found AI-POC-TE to accurately assess fibrosis and steatosis, comparable to conventional TE but with added values of portability, B-mode guidance and deep learning-based analytics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.