ArticleIEEE transactions on bio-medical engineering2019
Prediction of Abdominal Aortic Aneurysm Growth Using Dynamical Gaussian Process Implicit Surface.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review.BMC bioinformatics · 2022Pooled it
- Forecasting Patient-Specific Abdominal Aortic Aneurysm Geometry with Mixed-Effects Models.Diagnostics (Basel, Switzerland) · 2026Article
- Reconstructing blood flow in data-poor regimes: a vasculature network kernel for Gaussian process regression.Journal of the Royal Society, Interface · 2024Article
- Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics.Scientific reports · 2023Article
- Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning.Diagnostics (Basel, Switzerland) · 2021Article
- Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications.Computers in biology and medicine · 2021Article
- A generic physics-informed neural network-based constitutive model for soft biological tissues.Computer methods in applied mechanics and engineering · 2020Article
- Intraluminal thrombus effect on the progression of abdominal aortic aneurysms by using a multistate continuous-time Markov chain model.The Journal of international medical research · 2020Article
- Coronary artery decision algorithm trained by two-step machine learning algorithm.RSC advances · 2020Article
- Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration.IEEE journal of biomedical and health informatics · 2019Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
Abstract
objectiveWe propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way.
methodsWe adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time.
resultsA dataset of 7 subjects (4-7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach.
conclusionWe introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion. SIGNIFICANCE: The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs.
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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.