ArticleJournal of gastroenterology2022
Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts.
Article in Journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The use of machine learning in transarterial chemoembolisation/transarterial embolisation for patients with intermediate-stage hepatocellular carcinoma: a systematic review.La Radiologia medica · 2025Pooled it
- Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.Journal of imaging informatics in medicine · 2024Pooled it
- Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Clinical practice guidelines and real-life practice in hepatocellular carcinoma: A Japanese perspective.Clinical and molecular hepatology · 2023Guideline
- Concordance Between a Generative Artificial Intelligence Model and a Hepatobiliary Multidisciplinary Team in Hepatocellular Carcinoma Management: A Retrospective Study.Annals of surgical oncology · 2026Article
- Differentiation of focal liver lesions in contrast-enhanced ultrasound using a heuristic-guided hybrid machine-learning framework.Journal of medical ultrasonics (2001) · 2026Article
- [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
- Prompt engineering shapes diagnostic accuracy and explanation quality of LLM in oral lesion diagnosis: a prospective, expert-blinded benchmark study.Odontology · 2026Article
- Comparison of breast ultrasound image classification accuracy between convolutional neural networks and human experts using multicenter external validation cohort data.Journal of medical ultrasonics (2001) · 2026Article
- Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.Gut and liver · 2026Review
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Explainable fusion of EfficientNetB0 and ResNet50 for liver fibrosis staging in ultrasound imaging.Scientific reports · 2025Article
- Prompt-dependent performance of multimodal AI model in oral diagnosis: a comprehensive analysis of accuracy, narrative quality, calibration, and latency versus human experts.Scientific reports · 2025Article
- Improving ultrasound image classification accuracy of liver tumors using deep learning model with hepatitis virus infection information.Journal of medical ultrasonics (2001) · 2025Article
- Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.BMJ open gastroenterology · 2025Review
- Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models.Medicina (Kaunas, Lithuania) · 2025Article
- Validation of multiple deep learning models for colorectal tumor differentiation with endoscopic ultrasound images: a dual-center study.Journal of gastrointestinal oncology · 2025Article
- Mixture of Expert-Based SoftMax-Weighted Box Fusion for Robust Lesion Detection in Ultrasound Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Clinical Application of Artificial Intelligence in Ultrasound Imaging for Oncology.JMA journal · 2025Review
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
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Authors and funding
19 authors.
Funding
Abstract
backgroundUltrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the diagnosis of liver tumors in US.
methodsWe constructed three AI models based on still B-mode images: model-1 using 24,675 images, model-2 using 57,145 images, and model-3 using 70,950 images. A convolutional neural network was used to train the US images. The four-class liver tumor discrimination by AI, namely, cysts, hemangiomas, hepatocellular carcinoma, and metastatic tumors, was examined. The accuracy of the AI diagnosis was evaluated using tenfold cross-validation. The diagnostic performances of the AI models and human experts were also compared using an independent test cohort of video images.
resultsThe diagnostic accuracies of model-1, model-2, and model-3 in the four tumor types are 86.8%, 91.0%, and 91.1%, whereas those for malignant tumor are 91.3%, 94.3%, and 94.3%, respectively. In the independent comparison of the AIs and physicians, the percentages of correct diagnoses (accuracies) by the AIs are 80.0%, 81.8%, and 89.1% in model-1, model-2, and model-3, respectively. Meanwhile, the median percentages of correct diagnoses are 67.3% (range 63.6%-69.1%) and 47.3% (45.5%-47.3%) by human experts and non-experts, respectively.
conclusionThe performance of the AI models surpassed that of human experts in the four-class discrimination and benign and malignant discrimination of liver tumors. Thus, the AI models can help prevent human errors in US diagnosis.
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