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

• URL: http://arxiv.org/abs/2609.04365v1
• Date: Thu, 03 Sep 2026 18:25:16 GMT
• Title: Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models
• Abstract summary: Our framework integrates automated ultrasound data processing with modern deep learning architectures to identify patients at high risk of decompensation.<n>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.
• Score: 8.28072486483115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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