ArticleBriefings in bioinformatics2023
Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 32 citations in OpenAlex.
- LI-RADS-aligned artificial intelligence for liver cancer diagnosis: methods, evidence, and clinical readiness.Abdominal radiology (New York) · 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
- A generalist foundation model and database for open-world medical image segmentation.Nature biomedical engineering · 2026Article
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Development of a deep learning model for intussusception using point-of-care ultrasound.Frontiers in radiology · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- AI in radiology and interventions: a structured narrative review of workflow automation, accuracy, and efficiency gains of today and what's coming.International journal of computer assisted radiology and surgery · 2026Review
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- URFM: A general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis.iScience · 2025Article
- Current Advances in Classification, Prediction and Management of Microvascular Invasion in Hepatocellular Carcinoma.Journal of cellular and molecular medicine · 2025Review
- Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.BMJ open gastroenterology · 2025Review
- Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- 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 Application of Artificial Intelligence in Ultrasound Imaging for Oncology.JMA journal · 2025Review
- Artificial intelligence in hepatocellular carcinoma screening: applications and challenges.Frontiers in medicine · 2025Review
- Application of deep learning based on convolutional neural network model in multimodal ultrasound diagnosis of unexplained cervical lymph node enlargement.Frontiers in oncology · 2025Article
- Application of artificial intelligence in the diagnosis of hepatocellular carcinoma.eGastroenterology · 2023Review
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
Abstract
Recent developments of deep learning methods have demonstrated their feasibility in liver malignancy diagnosis using ultrasound (US) images. However, most of these methods require manual selection and annotation of US images by radiologists, which limit their practical application. On the other hand, US videos provide more comprehensive morphological information about liver masses and their relationships with surrounding structures than US images, potentially leading to a more accurate diagnosis. Here, we developed a fully automated artificial intelligence (AI) pipeline to imitate the workflow of radiologists for detecting liver masses and diagnosing liver malignancy. In this pipeline, we designed an automated mass-guided strategy that used segmentation information to direct diagnostic models to focus on liver masses, thus increasing diagnostic accuracy. The diagnostic models based on US videos utilized bi-directional convolutional long short-term memory modules with an attention-boosted module to learn and fuse spatiotemporal information from consecutive video frames. Using a large-scale dataset of 50 063 US images and video frames from 11 468 patients, we developed and tested the AI pipeline and investigated its applications. A dataset of annotated US images is available at https://doi.org/10.5281/zenodo.7272660.
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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.