ArticleScience talks2025
Augmented reality visualization of biomechanical wall stresses on abdominal aortic aneurysms using artificial intelligence.
Article in Science talks, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Optimizing predictive performance without sacrificing explainability: Comparing logistic regression and ensemble decision trees for abdominal aortic aneurysm repair outcomes.JVS-vascular insights · 2026Article
- Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study.Journal of vascular surgery cases and innovative techniques · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The number of medical images taken has continued to increase year over year for an aging population in the United States. It has been shown that patients understand their diagnoses better when shown a 2D or 3D image of their respective diseases. However, clinicians do not regularly show patients their images as it requires additional time and processing. In this experiment, we demonstrate the use of augmented reality to visualize abdominal aortic aneurysms using a previously developed artificial intelligence engine. Our group further expanded the number of cases used for training the stress prediction model to a total of 274 patients (206 used for training or ~ 5.4 million nodes, and 68 for testing or ~1.8 million nodes). Medical images undergo automated segmentation, and wall stresses are predicted on the 3D surface of aneurysms to view a heat map. The pipeline includes introducing elements into the Microsoft HoloLens 2 ecosystem to view models and additional analytics, enabling clinicians and patients to view the biomechanical status without the need for a computational or imaging expert. The proposed clinical workflow would allow a local server to process medical imaging data, generate point clouds, predict wall stresses on individual points, and create a 3D model with a colormap to view in augmented reality. The study revealed that neural networks and ensemble boosted tress models predicted the wall stresses more accurately (when compared to ground truth finite element analysis results). The approach is not limited to the HoloLens 2 ecosystem but can be used with other emerging augmented or virtual reality hardware systems.
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What Socratic holds
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 Socratic graph.