ArticleComputers in biology and medicine2020
Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference.
Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Geometry quantification for growth assessment of abdominal aortic aneurysms under surveillance.Scientific reports · 2026Article
- Physics-Based Growth and Remodeling Modeling for Virtual Abdominal Aortic Aneurysm Evolution and Growth Prediction.medRxiv : the preprint server for health sciences · 2026Article
- Reducing Emergency Diagnostic Uncertainty with TRACE: Triage and Risk Assessment via Cost Estimation.The western journal of emergency medicine · 2026Article
- Abdominal Aortic Aneurysm Pathomechanics, Current Understanding and Emerging Directions: A Perspective for 2025 and Beyond.Advances in experimental medicine and biology · 2026Review
- Sex-specific machine learning classification models improve outcome prediction for abdominal aortic aneurysms.Biology of sex differences · 2025Article
- Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics.Scientific reports · 2023Article
- Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems.Biomimetics (Basel, Switzerland) · 2023Review
- Computerized Differentiation of Growth Status for Abdominal Aortic Aneurysms: A Feasibility Study.Journal of cardiovascular translational research · 2023Article
- Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non-extensive statistics.Scientific reports · 2023Article
- Automatic segmentation of abdominal aortic aneurysms from CT angiography using a context-aware cascaded U-Net.Computers in biology and medicine · 2023Article
- Investigation of commonly used aortic aneurysm growth rate metrics: Comparing their suitability for clinical and research applications.PloS one · 2023Article
- Region-dependent mechanical characterization of porcine thoracic aorta with a one-to-many correspondence method to create virtual datasets using uniaxial tensile tests.Frontiers in bioengineering and biotechnology · 2022Article
- Non-extensive thermodynamic entropy to predict the dynamics behavior of COVID-19.Physica. B, Condensed matter · 2022Article
- Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms.Scientific reports · 2021Article
- Defining a master curve of abdominal aortic aneurysm growth and its potential utility of clinical management.Computer methods and programs in biomedicine · 2021Article
- Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications.Computers in biology and medicine · 2021Article
- Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).Annals of operations research · 2021Article
- Predictors of Abdominal Aortic Aneurysm Risks.Bioengineering (Basel, Switzerland) · 2020Review
- Statistical techniques for predicting rupture risk in abdominal aortic aneurysms: A contribution based on bootstrap.Science progressArticle
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5 authors.
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Abstract
objectiveFor small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.
methods106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler.
resultsThe follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were ±2.67, ±2.61 and ± 2.79 mm, respectively.
conclusionA computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth.
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