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

Guangyi Zhang; Peiyun Ni; Eugene Cheah; Rajat Chandra; Peng Guo; Raymond T. Chung; Anthony E. Samir

eess.IVSep 3, 2026

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

Authors: Guangyi Zhang, Peiyun Ni, Eugene Cheah, Rajat Chandra, Peng Guo, Raymond T. Chung, Anthony E. Samir

Organizations: Center for Ultrasound Research & Translation, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA · Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA · Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

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.

Figures & tables

Figure 1: B-mode data processing and modeling for cirrhosis decompensation prediction.

Panel of Figure 2 — Figure 2: Representative B-mode ultrasound images of the liver and spleen.

Family	Architecture	Pretrained	C-index (95% CI)
ResNet	ResNet-50	✗	0.58 (0.46–0.69)
	ResNet-50	✓	0.69 (0.58–0.79)
	ResNet-101	✗	0.59 (0.46–0.70)
	ResNet-101	✓	0.64 (0.52–0.75)
EfficientNet	EfficientNet-B2	✗	0.59 (0.47–0.71)
	EfficientNet-B2	✓	0.59 (0.47–0.70)

Table I : Concordance index (C-index) with 95% confidence intervals for imaging-based survival models. Models are grouped by architecture family and ordered with training from scratch shown before ImageNet pretraining. The benchmark serum-based method, MELD-Na, is included for comparison.

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Development and Evaluation of Ultrasound Image Learning Pipelines for MASLD Risk Stratification

Guangyi Zhang, Xiaohong Wang, Eugene Cheah, Peng Guo+3

Center for Ultrasound Research & Translation, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA · Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA

Ultrasound Imaging Medical Image Classification Clinical Risk Prediction

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Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 30% of the general population. Ultrasound-based imaging, including B-mode imaging and shear wave elastography (SWE), is widely used for noninvasive fibrosis assessment; however, the role of deep learning-based ultrasound image learning for MASLD risk stratification remains insufficiently characterized. In this study, we developed and evaluated ultrasound image learning pipelines using B-mode and SWE images for fibrosis staging and identification of patients with at-risk metabolic dysfunction-associated steatohepatitis (MASH). A total of 250 ultrasound examinations, one exam per subject, were included. Model performance was evaluated using 3-fold cross-validation with area under the receiver operating characteristic curve (AUROC). End-to-end SWE image learning achieved performance comparable to operator-guided SWE across fibrosis stages. Overall, SWE-based learning consistently outperformed B-mode image learning in fibrosis staging, with AUROC improvements from 0.64 (95%CI: [0.56, 0.72]) to 0.72 (95% CI: [0.65, 0.79]) for F>=2 (significant fibrosis, p=0.11), from 0.67 (95%CI: [0.58, 0.75]) to 0.78 (95% CI:[0.72, 0.85]) for F>=3 (advanced fibrosis, p=0.02), and from 0.69 (95%CI: [0.56, 0.82]) to 0.80 (95%CI: [0.72, 0.89]) for F4 (cirrhosis, p=0.10). These findings highlight the potential of SWE image learning for MASLD risk stratification.

Abstract

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