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Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models — arXiv:2609.04365

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.04365
License (as archived): CC0-1.0
Archived: 2026-10-16T22:26:06.408Z
SHA-256 of the archived text: 2ce462e6487567a3a681c4ef44a9ce6c6744699761ddb145ceb76960dd0c7037
Stable URL of this page: https://metraipolis.com/documento/dc_aaa0d310e8a2

How to cite

Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models — arXiv:2609.04365 — arXiv (en), https://arxiv.org/abs/2609.04365 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aaa0d310e8a2 (Metraipolis archive dc_aaa0d310e8a2, sha256 2ce462e6487567a3a681c4ef44a9ce6c6744699761ddb145ceb76960dd0c7037)

BibTeX

@misc{dcaaa0d310e8a2,
  title = {Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models — arXiv:2609.04365},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.04365},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aaa0d310e8a2; sha256 2ce462e6487567a3a681c4ef44a9ce6c6744699761ddb145ceb76960dd0c7037},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.04365. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-07 in cs.AI).
Categorie arXiv: Image and Video Processing (eess.IV) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.04365 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T22:18:11.484Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).