Observational studyPloS one2015
An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study.
Observational study in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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.
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Who cites it
27 citing papers in PubMed, 71 citations in OpenAlex.
- Vascular Precision Medicine: A Ray of Sunshine for the Therapies of the Present and Future.Angiology · 2026Article
- Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review.Journal of imaging informatics in medicine · 2026Review
- Machine Learning and Abdominal Aortic Aneurysm: A New Paradigm in Prediction and Prognosis after Endovascular Aneurysm Repair.Annals of vascular diseases · 2026Review
- Article
- 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
- AI-driven decision making for intravascular device selection in aortic disease. Current insights and prospects.Frontiers in cardiovascular medicine · 2025Review
- Editorial: The role of artificial intelligence technologies in revolutionizing and aiding cardiovascular medicine.Frontiers in cardiovascular medicine · 2025Article
- Review
- Review
- 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
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods.Diagnostics (Basel, Switzerland) · 2022Article
- A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.Applied soft computing · 2022Article
- An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.Biosensors · 2022Article
- ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.Computers in biology and medicine · 2022Article
- Development and Comparison of Multimodal Models for Preoperative Prediction of Outcomes After Endovascular Aneurysm Repair.Frontiers in cardiovascular medicine · 2022Article
- DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity.Diagnostics (Basel, Switzerland) · 2021Article
- Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories.Contrast media & molecular imaging · 2021Article
- Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images.PeerJ. Computer science · 2021Article
- A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundLifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques.
methodsPatients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk.
results761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p<0.001).
conclusionThis study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.
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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.