ArticleJournal of vascular and interventional radiology : JVIR2020
Three-Dimensional Augmented Reality Visualization Informs Locoregional Therapy in a Translational Model of Hepatocellular Carcinoma.
Article in Journal of vascular and interventional radiology : JVIR, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- A systematic review of immersive educational technologies in medical physics and radiation physics.Frontiers in medicine · 2024Pooled it
- Utility of optical see-through head mounted displays in augmented reality-assisted surgery: A systematic review.Medical image analysis · 2022Pooled it
- Augmented reality-assisted cholangioscopy for biliary interventions: initial clinical experience in four cases.Surgical endoscopy · 2026Article
- Virtual and Augmented Reality in Interventional Radiology: Current Applications, Challenges, and Future Directions.Techniques in vascular and interventional radiology · 2023Article
- Augmented Reality Demonstration Survey Results From a Veteran Affairs Medical Center.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2023Article
- Multicenter assessment of augmented reality registration methods for image-guided interventions.La Radiologia medica · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo evaluate the utility of visualizing preprocedural MR images in 3-dimensional (3D) space using augmented reality (AR) before transarterial embolization of hepatocellular carcinoma (HCC) in a preclinical model. MATERIALS AND
methodsA total of 28 rats with diethylnitrosamine-induced HCCs > 5 mm treated with embolization were included in a prospective study. In 12 rats, 3D AR visualization of preprocedural MR images was performed before embolization. Procedural metrics including catheterization time and radiation exposure were compared vs a prospective cohort of 16 rats in which embolization was performed without AR. An additional cohort of 15 retrospective cases was identified and combined with the prospective control cohort (n = 31) to improve statistical power.
resultsA 37% reduction in fluoroscopy time, from 11.7 min to 7.4 minutes, was observed with AR when compared prospectively, which did not reach statistical significance (P = .12); however, when compared with combined prospective and retrospective controls, the reduction in fluoroscopy time from 14.1 min to 7.4 minutes (48%) was significant (P = .01). A 27% reduction in total catheterization time, from 42.7 minutes to 31.0 minutes, was also observed with AR when compared prospectively, which did not reach statistical significance (P = .11). No significant differences were seen in dose-area product or air kerma prospectively.
conclusionsThree-dimensional AR visualization of preprocedural imaging may aid in the reduction of procedural metrics in a preclinical model of transarterial embolization. These data support the need for further studies to evaluate the potential of AR in endovascular oncologic interventions.
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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.