ArticleDiagnostics (Basel, Switzerland)2023
Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Contrast-Enhanced Ultrasound of the Liver: A Systematic Review.Digestion · 2025Pooled it
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment.World journal of gastroenterology · 2025Review
- Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.BMJ open gastroenterology · 2025Review
- Impact of the COVID-19 Pandemic on Gut Cancer Admissions and Management: A Comparative Study of Two Pandemic Years to a Similar Pre-Pandemic Period.Healthcare (Basel, Switzerland) · 2025Article
- Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.Life (Basel, Switzerland) · 2025Review
- Prevalence and Risk Factors for Portal Cavernoma in Adult Patients with Portal Vein Thrombosis.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Diagnostic performance of AI-based models versus physicians among patients with hepatocellular carcinoma: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2024Review
- Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know.Journal of clinical and experimental hepatologyReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Contrast-enhanced ultrasound (CEUS) is widely used in the characterization of liver tumors; however, the evaluation of perfusion patterns using CEUS has a subjective character. This study aims to evaluate the accuracy of an automated method based on CEUS for classifying liver lesions and to compare its performance with that of two experienced clinicians. The system used for automatic classification is based on artificial intelligence (AI) algorithms. For an interpretation close to the clinical setting, both clinicians knew which patients were at high risk for hepatocellular carcinoma (HCC), but only one was aware of all the clinical data. In total, 49 patients with 59 liver tumors were included. For the benign and malignant classification, the AI model outperformed both clinicians in terms of specificity (100% vs. 93.33%); still, the sensitivity was lower (74% vs. 93.18% vs. 90.91%). In the second stage of multiclass diagnosis, the automatic model achieved a diagnostic accuracy of 69.93% for HCC and 89.15% for liver metastases. Readers demonstrated greater diagnostic accuracy for HCC (83.05% and 79.66%) and liver metastases (94.92% and 96.61%) compared to the AI system; however, both were experienced sonographers. The AI model could potentially assist and guide less-experienced clinicians to discriminate malignant from benign liver tumors with high accuracy and specificity.
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