ArticleEJVES short reports2018
Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans.
Article in EJVES short reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
26 citing papers in PubMed.
- Vascular Precision Medicine: A Ray of Sunshine for the Therapies of the Present and Future.Angiology · 2026Article
- Application of radiomics in abdominal aortic aneurysm and endovascular aneurysm repair-related adverse events imaging: a systematic review.CVIR endovascular · 2026Review
- A Predictive Model for Major Adverse Aortic Events in Patients with Abdominal Aortic Aneurysm Using Clinical and Biomarker Data.International journal of molecular sciences · 2026Article
- Morphological risk factors of parent arteries in intracranial aneurysm formation: an interpretable machine learning analysis.Neuroradiology · 2026Article
- A Comparative Study of Unsupervised and Deep Learning Methods for Automatic Segmentation of Abdominal Aortic Aneurysm on CT Images: Preliminary Results.Advances in experimental medicine and biology · 2026Article
- Abdominal Aortic Aneurysm Pathomechanics, Current Understanding and Emerging Directions: A Perspective for 2025 and Beyond.Advances in experimental medicine and biology · 2026Review
- Machine Learning and Abdominal Aortic Aneurysm: A New Paradigm in Prediction and Prognosis after Endovascular Aneurysm Repair.Annals of vascular diseases · 2026Review
- Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.Scientific reports · 2025Article
- Atherosclerotic Abdominal Aortic Aneurysms on Computed Tomography Angiography: A Narrative Review on Spectrum of Findings, Structured Reporting, Treatment, Secondary Complications and Differential Diagnosis.Diagnostics (Basel, Switzerland) · 2025Review
- Persistence diagrams for exploring the shape variability of abdominal aortic aneurysms.Scientific reports · 2024Article
- Review
- Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment.Journal of cardiovascular development and disease · 2024Review
- Review
- Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics.Scientific reports · 2023Article
- Machine learning model predicts the occurrence of acute kidney injury after open surgery for abdominal aortic aneurysm repair.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm.International journal of computer assisted radiology and surgery · 2022Article
- The role of phosphoinositide 3-kinases in immune-inflammatory responses: potential therapeutic targets for abdominal aortic aneurysm.Cell cycle (Georgetown, Tex.) · 2022Review
- Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning.Journal of vascular surgery cases and innovative techniques · 2022Article
- Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data.Journal of personalized medicine · 2022Article
- Imaging and Surveillance of Chronic Aortic Dissection: A Scientific Statement From the American Heart Association.Circulation. Cardiovascular imaging · 2022Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAccurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques.
methodsThe Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway. In addition to the AAA diameter, FMD was systemically measured in these patients. A benchmark machine learning technique (non-linear Kernel support vector regression) was applied to predict future AAA growth in
resultsProspective growth data were recorded at 12 months (360 ± 49 days) in 94 patients. Of these, growth data were further recorded at 24 months (718 ± 81 days) in 79 patients. The average growth in AAA diameter was 3.4% at 12 months, and 2.8% per year at 24 months. The algorithm predicted the individual's AAA diameter to within 2 mm error in 85% and 71% of patients at 12 and 24 months.
conclusionsThe data highlight the utility of FMD as a biomarker for AAA and the value of machine learning techniques for AAA research in the new era of precision medicine.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.