Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models

Abstract:Decompensation represents a critical transition in the course of cirrhosis, yet clinicians have limited non-invasive tools to reliably predict its onset. In this study, we propose a novel imaging-based approach that leverages large-scale computer vision models to analyze routine abdominal ultrasound images and extract predictive features beyond those captured by traditional laboratory-based risk scores. Ultrasound is widely available, low cost, and suitable for longitudinal surveillance, making it an attractive modality for scalable risk stratification and long-term follow-up. Our framework integrates automated ultrasound data processing with modern deep learning architectures to identify patients at high risk of decompensation prior to the occurrence of clinical deterioration. This non-invasive strategy offers a practical complement to existing clinical scoring systems and may enable earlier, more proactive management of patients with compensated cirrhosis.

6 pages, 2 figures. Accepted and presented at IEEE EMBC 2026

Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.04365 [eess.IV]

(or arXiv:2609.04365v1 [eess.IV] for this version)

https://doi.org/10.48550/arXiv.2609.04365 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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