ArticleEuropean radiology2023
A deep learning model with data integration of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical parameters for diagnosing significant liver fibrosis in patients with chronic hepatitis B.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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7 citing papers in PubMed, 10 citations in OpenAlex.
- Early hemodynamic assessment and longitudinal imaging for predicting graft outcomes post-transplant.Annals of medicine · 2026Article
- Multimodal artificial intelligence models for liver fibrosis staging: a scoping review.Abdominal radiology (New York) · 2026Review
- A nomogram model integrating ultrasound-based multimodal radiomics features and clinical indexes for diagnosing significant hepatic fibrosis in AILD patients.Abdominal radiology (New York) · 2026Article
- A comprehensive narrative review of artificial intelligence use in the diagnosis and management of metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Review
- A comprehensive evaluation system for ultrasound-guided infusion of human umbilical cord-derived MSCs in liver cirrhosis patients.Stem cells translational medicine · 2025Article
- Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.Scientific reports · 2023Article
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Corrections and comments
- Erratum issued
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo develop and investigate a deep learning model with data integration of ultrasound contrast-enhanced micro-flow (CEMF) cines, B-mode images, and patients' clinical parameters to improve the diagnosis of significant liver fibrosis (≥ F2) in patients with chronic hepatitis B (CHB).
methodsOf 682 CHB patients who underwent ultrasound and histopathological examinations between October 2016 and May 2020, 218 subjects were included in this retrospective study. We devised a data integration-based deep learning (DIDL) model for assessing ≥ F2 in CHB patients. The model contained three convolutional neural network branches to automatically extract features from ultrasound CEMF cines, B-mode images, and clinical data. The extracted features were fused at the backend of the model for decision-making. The diagnostic performance was evaluated across fivefold cross-validation and compared against the other methods in terms of the area under the receiver operating characteristic curve (AUC), with histopathological results as the reference standard.
resultsThe mean AUC achieved by the DIDL model was 0.901 [95% CI, 0.857-0.939], which was significantly higher than those of the comparative methods, including the models trained by using only CEMF cines (0.850 [0.794-0.893]), B-mode images (0.813 [0.754-0.862]), or clinical data (0.757 [0.694-0.812]), as well as the conventional TIC method (0.752 [0.689-0.808]), APRI (0.792 [0.734-0.845]), FIB-4 (0.776 [0.714-0.829]), and visual assessments of two radiologists (0.812 [0.754-0.862], and 0.800 [0.739-0.849]), all ps < 0.01, DeLong test.
conclusionThe DIDL model with data integration of ultrasound CEMF cines, B-mode images, and clinical parameters showed promising performance in diagnosing significant liver fibrosis for CHB patients. KEY POINTS: • The combined use of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical data in a deep learning model has potential to improve the diagnosis of significant liver fibrosis. • The deep learning model with the fusion of features extracted from multimodality data outperformed the conventional methods including mono-modality data-based models, the time-intensity curve-based recognizer, fibrosis biomarkers, and visual assessments by experienced radiologists. • The interpretation of the feature attention maps in the deep learning model may help radiologists get better understanding of liver fibrosis-related features and hence potentially enhancing their diagnostic capacities.
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