ArticleScientific reports2024
Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A multitask framework for automated multi-frame right upper quadrant ultrasound interpretation and clinical decision support.Nature communications · 2026Article
- Pilot feasibility study of deep learning-based detection of malignant liver lesions in intraoperative ultrasound imaging.Surgical endoscopy · 2026Article
- Artificial Intelligence in Surveillance of Hepatocellular Carcinoma: Hype or Reality?Journal of gastroenterology and hepatology · 2026Article
- Virus infections and cancers: from mechanisms to therapeutics.Molecular biomedicine · 2026Review
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- Understanding Liver and Digestive Diseases: A Paved Road to Improve Diagnosis, Management, and Treatment.Exploration of digestive diseases · 2026Article
- [Diagnosis and treatment of liver diseases in the era of artificial intelligence].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Review
- Review
- Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.BMJ open gastroenterology · 2025Review
- Diagnosis of Cholangiocarcinoma: The New Biological and Technological Horizons.Diagnostics (Basel, Switzerland) · 2025Review
- Multi-center study: ultrasound-based deep learning features for predicting Ki-67 expression in breast cancer.Scientific reports · 2025Article
- Review
- Mixture of Expert-Based SoftMax-Weighted Box Fusion for Robust Lesion Detection in Ultrasound Imaging.Diagnostics (Basel, Switzerland) · 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
- Artificial intelligence in hepatocellular carcinoma screening: applications and challenges.Frontiers in medicine · 2025Review
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
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Authors and funding
10 authors.
Funding
Abstract
The effectiveness of ultrasonography (USG) in liver cancer screening is partly constrained by the operator's expertise. We aimed to develop and evaluate an AI-assisted system for detecting and classifying focal liver lesions (FLLs) from USG images. This retrospective study incorporated 26,288 USG images from 5444 patients to train YOLOv5 model for FLLs detection and classification of seven different types of FLLs, including hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), focal fatty infiltration, focal fatty sparing (FFS), cyst, hemangioma, and regenerative nodules. AI model performance was assessed for detection and diagnosis of the FLLs on a per-image and per-lesion basis. The AI achieved an overall FLLs detection rate of 84.8% (95%CI:83.3-86.4), with consistent performance for FLLs ≤ 1 cm and > 1 cm. It also exhibited sensitivity and specificity for distinguishing malignant FLLs from other benign FLLs at 97.0% (95%CI:95. 9-98.2) and 97.0% (95%CI:95.9-98.1), respectively. Among specific FLL types, CCA detection rate was at 92.2% (95%CI:88.0-96.4), followed by FFS at 89.7% (95%CI:87.1-92.3), and HCC at 82.3% (95%CI:77.1-87.5). The specificities and NPVs for regenerative nodules were 100% and 99.9% (95%CI:99.8-100.0), respectively. Our AI model can potentially assist physicians in FLLs detection and diagnosis during USG examinations. Further external validation is needed for clinical application.
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