973 research outputs found
Information Maximizing Component Analysis of Left Ventricular Remodeling Due to Myocardial Infarction
Background: Although adverse left ventricular shape changes (remodeling) after myocardial infarction (MI) are predictive of morbidity and mortality, current clinical assessment is limited to simple mass and volume measures, or dimension ratios such as length to width ratio. We hypothesized that information maximizing component analysis (IMCA), a supervised feature extraction method, can provide more efficient and sensitive indices of overall remodeling.
Methods: IMCA was compared to linear discriminant analysis (LDA), both supervised methods, to extract the most discriminatory global shape changes associated with remodeling after MI. Finite element shape models from 300 patients with myocardial infarction from the DETERMINE study (age 31–86, mean age 63, 20 % women) were compared with 1991 asymptomatic cases from the MESA study (age 44–84, mean age 62, 52 % women) available from the Cardiac Atlas Project. IMCA and LDA were each used to identify a single mode of global remodeling best discriminating the two groups. Logistic regression was employed to determine the association between the remodeling index and MI. Goodness-of-fit results were compared against a baseline logistic model comprising standard clinical indices.
Results: A single IMCA mode simultaneously describing end-diastolic and end-systolic shapes achieved best results (lowest Deviance, Akaike information criterion and Bayesian information criterion, and the largest area under the receiver-operating-characteristic curve). This mode provided a continuous scale where remodeling can be quantified and visualized, showing that MI patients tend to present larger size and more spherical shape, more bulging of the apex, and thinner wall thickness.
Conclusions: IMCA enables better characterization of global remodeling than LDA, and can be used to quantify progression of disease and the effect of treatment. These data and results are available from the Cardiac Atlas Project (http://www.cardiacatlas.org)
Orthogonal Decomposition of Left Ventricular Remodeling in Myocardial Infarction
BACKGROUND: Left ventricular size and shape are important for quantifying cardiac remodeling in response to cardiovascular disease. Geometric remodeling indices have been shown to have prognostic value in predicting adverse events in the clinical literature, but these often describe interrelated shape changes. We developed a novel method for deriving orthogonal remodeling components directly from any (moderately independent) set of clinical remodeling indices.
RESULTS: Six clinical remodeling indices (end-diastolic volume index, sphericity, relative wall thickness, ejection fraction, apical conicity, and longitudinal shortening) were evaluated using cardiac magnetic resonance images of 300 patients with myocardial infarction, and 1991 asymptomatic subjects, obtained from the Cardiac Atlas Project. Partial least squares (PLS) regression of left ventricular shape models resulted in remodeling components that were optimally associated with each remodeling index. A Gram-Schmidt orthogonalization process, by which remodeling components were successively removed from the shape space in the order of shape variance explained, resulted in a set of orthonormal remodeling components. Remodeling scores could then be calculated that quantify the amount of each remodeling component present in each case. A one-factor PLS regression led to more decoupling between scores from the different remodeling components across the entire cohort, and zero correlation between clinical indices and subsequent scores.
CONCLUSIONS: The PLS orthogonal remodeling components had similar power to describe differences between myocardial infarction patients and asymptomatic subjects as principal component analysis, but were better associated with well-understood clinical indices of cardiac remodeling. The data and analyses are available from www.cardiacatlas.org
Orthogonal decomposition of left ventricular remodeling in myocardial infarction
Left ventricular size and shape are important for quantifying cardiac remodeling in response to cardiovascular disease. Geometric remodeling indices have been shown to have prognostic value in predicting adverse events in the clinical literature, but these often describe interrelated shape changes. We developed a novel method for deriving orthogonal remodeling components directly from any (moderately independent) set of clinical remodeling indices. Results: Six clinical remodeling indices (end-diastolic volume index, sphericity, relative wall thickness, ejection fraction, apical conicity, and longitudinal shortening) were evaluated using cardiac magnetic resonance images of 300 patients with myocardial infarction, and 1991 asymptomatic subjects, obtained from the Cardiac Atlas Project. Partial least squares (PLS) regression of left ventricular shape models resulted in remodeling components that were optimally associated with each remodeling index. A Gram–Schmidt orthogonalization process, by which remodeling components were successively removed from the shape space in the order of shape variance explained, resulted in a set of orthonormal remodeling components. Remodeling scores could then be calculated that quantify the amount of each remodeling component present in each case. A one-factor PLS regression led to more decoupling between scores from the different remodeling components across the entire cohort, and zero correlation between clinical indices and subsequent scores. Conclusions: The PLS orthogonal remodeling components had similar power to describe differences between myocardial infarction patients and asymptomatic subjects as principal component analysis, but were better associated with well-understood clinical indices of cardiac remodeling. The data and analyses are available from www.cardiacatlas.org
Machine learning approaches to model cardiac shape in large-scale imaging studies
Recent improvements in non-invasive imaging, together with the introduction of fully-automated segmentation algorithms and big data analytics, has paved the way for large-scale population-based imaging studies. These studies promise to increase our understanding of a large number of medical conditions, including cardiovascular diseases. However, analysis of cardiac shape in such studies is often limited to simple morphometric indices, ignoring large part of the information available in medical images. Discovery of new biomarkers by machine learning has recently gained traction, but often lacks interpretability. The research presented in this thesis aimed at developing novel explainable machine learning and computational methods capable of better summarizing shape variability, to better inform association and predictive clinical models in large-scale imaging studies.
A powerful and flexible framework to model the relationship between three-dimensional (3D) cardiac atlases, encoding multiple phenotypic traits, and genetic variables is first presented. The proposed approach enables the detection of regional phenotype-genotype associations that would be otherwise neglected by conventional association analysis. Three learning-based systems based on deep generative models are then proposed. In the first model, I propose a classifier of cardiac shapes which exploits task-specific generative shape features, and it is designed to enable the visualisation of the anatomical effect these features encode in 3D, making the classification task transparent. The second approach models a database of anatomical shapes via a hierarchy of conditional latent variables and it is capable of detecting, quantifying and visualising onto a template shape the most discriminative anatomical features that characterize distinct clinical conditions. Finally, a preliminary analysis of a deep learning system capable of reconstructing 3D high-resolution cardiac segmentations from a sparse set of 2D views segmentations is reported. This thesis demonstrates that machine learning approaches can facilitate high-throughput analysis of normal and pathological anatomy and of its determinants without losing clinical interpretability.Open Acces
Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study
Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.British Heart Foundation (PG/14/89/31194)National Institutes of Health (USA) 1R01HL12175
Application of Engineered Porosity and Modified Effective Moduli to the Design of Orthopaedic Implants
Commercially available orthopaedic implants have a bending stiffness (flexural rigidity) that is at least 10 times greater than cortical bone. Effects of this stiffness mismatch have been extensively studied relative to total hip arthroplasty (THA). Clinical experience with THA has shown that stiffness mismatch is the primary cause of accelerated bone resorption due to the stress shielding, resulting in sub-optimal bone loading, aseptic loosening and inadequate bone support for a future revision implant.
Attempts to incorporate design features that reduce the flexural rigidity of implants have yielded inconsistent results or failures due to biomaterial incompatibilities and practical manufacturing complications. The recent development of additive manufacturing (AM) processes allow the fabrication of closed-cell porous Ti or CoCr microstructures as a practical means of fabrication while reducing implant stiffness.
The use of engineered porosity to modify flexural rigidity requires an ability to predict moduli from microstructural parameters. The literature is replete with different formulas which are often contradictory; existing equations relating porosity to effective moduli are generally interpretive and not predictive.
This study applied finite element methods to three-dimensional porous structures with different arrangements of spheroidal voids. The resulting data show that the effective Young\u27s modulus varies linearly with &psi, the ratio of pore radius to center-to-center dimension, for a porosity range of 20 to 50%. In addition, the arrangement of spherical voids was found to have only a minimal effect on the resultant Young\u27s modulus. Predictive equations for Poisson\u27s ratio are second-order and dependent upon the void arrangement. The effect of changes in loading direction on moduli indicate that the three microstructures evaluated in this study are anisotropic, with anisotropy increasing with both ψ and volume porosity. The predictive equations developed in this study were validated with AM fabrication and testing of prototypical Ti6Al4V spinal rods. Constructs of a rhombohedral (FCC) pore arrangement with 30% porosity showed an effective reduction of ~ 50% in Young\u27s modulus. Predicted values for flexural rigidity fell within 95% confidence intervals for the tested porous Ti6Al4V constructs, confirming a design methodology with the potential of reducing the flexural rigidity, and resulting bone resorption, of orthopaedic implants
Periodic repolarization dynamics as predictor of risk for sudden cardiac death in chronic heart failure patients
The two most common modes of death among chronic heart failure (CHF) patients are sudden cardiac death (SCD) and pump failure death (PFD). Periodic repolarization dynamics (PRD) quantifies low-frequency oscillations in the T wave vector of the electrocardiogram (ECG) and has been postulated to reflect sympathetic modulation of ventricular repolarization. This study aims to evaluate the prognostic value of PRD to predict SCD and PFD in a population of CHF patients. 20-min high-resolution (1000 Hz) ECG recordings from 569 CHF patients were analyzed. Patients were divided into two groups, PRD+ and PRD-, corresponding to PRD values above and below the optimum cutoff point of PRD in the study population. Univariate Cox regression analysis showed that SCD risk in the PRD+ group was double the risk in the PRD- group [ hazard ratio (95% CI) 2.001 (1.127-3.554), p < 0.05]. The combination of PRD with other Holter-based ECG indices, such as turbulence slope (TS) and index of average alternans (IAA), improved SCD prediction by identifying groups of patients at high SCD risk. PFD could be predicted by PRD only when combined with TS [hazard ratio 2.758 (1.572-4.838), p < 0.001]. In conclusion, the combination of PRD with IAA and TS can be used to stratify the risk for SCD and PFD, respectively, in CHF patients
Combination of Polyaffine Transformations and Supervised Learning for the Automatic Diagnosis of LV Infarct
International audienceIn this article, we present an application of the polyaffine transformations to classify a population of hearts with myocardial infarction. Polyaffine transformations aim at representing motion by the combination of a limited number of affine transformations defined locally on a regional division of the space. We show that these transformations not only serve as a first (non-learnt) dimension reduction, but also allow to interpret each of the parameters and relate them to known clinical parameters. Then, we use standard supervised learning algorithms on these parameters to classify the population between infarcted and non-infarcted subjects. The method is applied on the STACOM statistical shape modeling labeled data consisting of 200 cases, comprising the same number of healthy subjects and patients with infarct. We train classifiers using different standard machine learning algorithms. Finally, we validate our method with 10-fold cross-validation and get more than 95% of correct classification on yet-unseen data. The method is promising and ready to be tested on the remaining 200 test cases of the challenge
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