ArticleCancers2021
An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Quantitative pancreatic MRI in diabetes mellitus: current advances and future directions.Frontiers in endocrinology · 2026Review
- Modified Appleby procedure for pancreatic body cancer: Retrograde gastric and hepatic perfusion via a replaced left hepatic artery - A vascular paradigm shift: A case report.International journal of surgery case reports · 2025Article
- The role of artificial intelligence in pancreatic surgery: Current and future perspectives.Annals of hepato-biliary-pancreatic surgery · 2025Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Dendritic cell vaccination combined with irreversible electroporation for treating pancreatic cancer-a narrative review.Annals of translational medicine · 2024Review
- How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications.Bioengineering (Basel, Switzerland) · 2023Review
- Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis.World journal of gastroenterology · 2023Review
- Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver.World journal of gastroenterology · 2023Review
- Review
- Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies.Tomography (Ann Arbor, Mich.) · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.
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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.