aceIntegrating Plain 2D Ultrasound and Clinical Data for Identification of At-Risk MASH: A Multicenter Proof of Concept Study
Background Identifying people with at-risk metabolic dysfunction-associated steatohepatitis (MASH) is a major challenge. We aimed to use deep learning to develop a convolutional neural network (CNN) model to facilitate the detection of at-risk MASH. Methods A multi-center cohort study was conducted across five tertiary hospitals in Japan and China. Clinical data and conventional ultrasound images were retrospectively collected. Organ segmentation using YOLO version 11 with quality control was performed, followed…
A CNN trained on conventional ultrasound images and clinical data estimated FAST and AGILE 3+ risk scores with high discrimination at rule-out and rule-in cut-offs, which may help where transient elastography is scarce.
The study was retrospective and described as a proof of concept, and the model targets composite risk scores rather than a direct reference diagnosis; agreement with AGILE 3+ was only moderate.
Evidence
- Peer-reviewedJournal of Gastroenterology and Hepatology2026-10-16
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Truvace Impact Record TRV-2026-1948, v1: “Integrating Plain 2D Ultrasound and Clinical Data for Identification of At-Risk MASH: A Multicenter Proof of Concept Study.” Truvace, 2026-10-19. /record/TRV-2026-1948 (accessed at citation time). sha256 a9e6a256277515be…
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