ArticleEBioMedicine2020
Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it, 112 citations in OpenAlex.
- Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- [Application and prospects of artificial intelligence from tool empowerment to paradigm reconstruction for whole-course diagnosis and treatment of liver diseases by ultrasound].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- Primary Liver Cancer Trends Worldwide and in China: Analysis of GLOBOCAN 2022 Data and Disease Management Implications.Portal hypertension & cirrhosis · 2026Review
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Review
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- The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound.Bioengineering (Basel, Switzerland) · 2025Article
- AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening.NPJ digital medicine · 2025Article
- Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.BMJ open gastroenterology · 2025Review
- New Insights into the Diagnosis and Treatment of Hepatocellular Carcinoma.Biomedicines · 2025Review
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic study.EClinicalMedicine · 2025Article
- Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?Diagnostics (Basel, Switzerland) · 2025Review
- An Artificial Intelligence Pipeline for Hepatocellular Carcinoma: From Data to Treatment Recommendations.International journal of general medicine · 2025Review
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- Multidisciplinary Team Management of Hepatocellular Carcinoma in the MENA Region: Current Practices, Challenges, and Gaps.Journal of hepatocellular carcinoma · 2025Article
- Enhancing cancer risk awareness and screening management through artificial intelligence: a narrative review.Frontiers in oncology · 2025Review
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
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Authors and funding
27 authors at 15 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs. MATERIALS AND
methodsThis study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 4:1 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with that of contrast enhanced computed tomography (CT)/magnetic resonance image (MRI) and 236 radiologists, respectively.
findingsThe AUC of Model
interpretationDCNN-US with high sensitivity and specificity in diagnosing FLLs shows its potential to assist less-experienced radiologists in improving their performance and lowering their dependence on sectional imaging in liver cancer diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.