Identification of Hepatic Fibrosis and Steatosis via A Point-of-Care Transient Elastography System With Integrated AI.

BACKGROUND & AIMS: Transient 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. METHODS: This 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). RESULTS: LSM 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. CONCLUSION: We 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.