Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures

Integrating ensemble and machine learning models for early prediction of pneumonia mortality using laboratory tests

Background

The recent use of artificial intelligence (AI) in medical research is noteworthy. However, most research has focused on medical imaging. Although the importance of laboratory tests in the clinical field is acknowledged by clinicians, they are undervalued in medical AI research. Our study aims to develop an early prediction AI model for pneumonia mortality, primarily using laboratory test results.

Materials and methods

We developed a mortality prediction model using initial laboratory results and basic clinical information of patients with pneumonia. Several machine learning (ML) models and a deep learning method-multilayer perceptron (MLP)-were selected for model development. The area under the receiver operating characteristic curve (AUROC) and F1-score were optimized to improve model performance. In addition, an ensemble model was developed by blending several models to improve the prediction performance. We used 80,940 data instances for model development.

Results

Among the ML models, XGBoost exhibited the best performance (AUROC = 0.8989, accuracy = 0.88, F1-score = 0.80). MLP achieved an AUROC of 0.8498, accuracy of 0.86, and F1-score of 0.75. The performance of the ensemble model was the best among the developed models, with an AUROC of 0.9006, accuracy of 0.90, and F1-score of 0.81. Several laboratory tests were conducted to identify risk factors that affect pneumonia mortality using the "Feature importance" technique and SHapley Additive exPlanations. We identified several laboratory results, including systolic blood pressure, serum glucose level, age, aspartate aminotransferase-to-alanine aminotransferase ratio, and monocyte-to-lymphocyte ratio, as significant predictors of mortality in patients with pneumonia.

Conclusions

Our study demonstrates that the ensemble model, incorporating XGBoost, CatBoost, and LGBM techniques, outperforms individual ML and deep learning models in predicting pneumonia mortality. Our findings emphasize the importance of integrating AI techniques to leverage laboratory test data effectively, offering a promising direction for advancing AI applications in medical research and clinical decision-making.

Morphology of Abdominal Aortic Aneurysms and Correlation with Biomechanical Tests of Aneurysmal Wall Fragments

BACKGROUND

Evaluate how specific morphologic aspects of abdominal aortic aneurysms (AAAs), including asymmetries, curvatures, tortuosities, and angulations, among others can influence the intrinsic biomechanical properties of the AAA's wall. This study analyzed the correlation of geometric measurements (1-dimensional, 2-dimensional, and 3-dimensional) of preoperative tomographic images of AAA with uniaxial biomechanical tests of the arterial wall fragments of these AAA obtained in open surgical repair of aneurysms.

METHODS

It was a multicenter, experimental, and observational study, and initially 54 individuals were selected who underwent open surgical of AAA, with valid biomechanical tests of the anterior wall of the AAA. Seven individuals were excluded because they had poor preoperative quality computed tomography scans and/or artifacts that impeded image segmentation and extraction of AAA geometric indices. The aortic fragments were subjected to uniaxial biomechanical destructive tests to obtain the following data: maximum load, failure stress, failure tension, failure strain energy, strain, and fragment thickness. In the same patients, preoperative computed tomography scans were performed with the extraction of 26 geometric indices, subdivided into 9 1-dimensional indices, 6 2-dimensional indices, and 11 3-dimensional indices. Data were subjected to statistical analysis using SPSS version 28.

RESULTS

Comparing ruptured and unruptured AAA, no statistical difference was observed between the biomechanical and geometric parameters. The fragment thickness of the ruptured AAA was lower than that of the unruptured AAA (P < 0.05). By comparing tomographic geometric indices and biomechanical parameters of the aortic fragments using Pearson's coefficient, positive and linear correlations (P < 0.05) were observed between the geometric variable maximum diameter (Dmax) of the AAA with maximum load (r = 0.408), failure tension (r = 0.372), and failure stress (r = 0.360). Positive and linear correlations were also observed between the variable diameter/height ratio (DHr) and the maximum load (r = 0.360), failure tension (r = 0.354), and failure stress (r = 0.289). The geometric variable DHr was dependent and correlated with Dmax. Simple regression analysis showed that R2 varied between 8.3% and 16.7%, and all models were significant (P < 0.05).

CONCLUSIONS

Dmax and DHr were linearly and positively correlated with the resistance parameters (maximum load, failure tension, and failure stress) of the AAA fragments. The DHr variable is dependent and correlated with Dmax. There was no correlation between the other geometric indices and the biomechanical parameters of the AAA wall. The asymmetries did not globally influence the biomechanics of AAA wall; however, they may influence regionally. Larger AAAs were stronger than smaller ones. Therefore, such findings may point toward Dmax is still the main geometric parameter, which influences the anterior wall, and possibly globally in the AAA.

Application and utility of boosting machine learning model based on laboratory test in the differential diagnosis of non-COVID-19 pneumonia and COVID-19

BACKGROUND

Non-Coronavirus disease 2019 (COVID-19) pneumonia and COVID-19 have similar clinical features but last for different periods, and consequently, require different treatment protocols. Therefore, they must be differentially diagnosed. This study uses artificial intelligence (AI) to classify the two forms of pneumonia using mainly laboratory test data.

METHODS

Various AI models are applied, including boosting models known for deftly solving classification problems. In addition, important features that affect the classification prediction performance are identified using the feature importance technique and SHapley Additive exPlanations method. Despite the data imbalance, the developed model exhibits robust performance.

RESULTS

eXtreme gradient boosting, category boosting, and light gradient boosted machine yield an area under the receiver operating characteristic of 0.99 or more, accuracy of 0.96-0.97, and F1-score of 0.96-0.97. In addition, D-dimer, eosinophil, glucose, aspartate aminotransferase, and basophil, which are rather nonspecific laboratory test results, are demonstrated to be important features in differentiating the two disease groups.

CONCLUSIONS

The boosting model, which excels in producing classification models using categorical data, excels in developing classification models using linear numerical data, such as laboratory tests. Finally, the proposed model can be applied in various fields to solve classification problems.

Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records

BACKGROUND

An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow efficient allocation of limited medical resources. We developed an early mortality prediction ensemble model for COVID-19 using AI models with initial chest X-ray and electronic health record (EHR) data.

RESULTS

We used convolutional neural network (CNN) models (Inception-ResNet-V2 and EfficientNet) for chest X-ray analysis and multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and random forest (RF) models for EHR data analysis. The Gradient-weighted Class Activation Mapping and Shapley Additive Explanations (SHAP) methods were used to determine the effects of these features on COVID-19. We developed an ensemble model (Area under the receiver operating characteristic curve of 0.8698) using a soft voting method with weight differences for CNN, XGBoost, MLP, and RF models. To resolve the data imbalance, we conducted F1-score optimization by adjusting the cutoff values to optimize the model performance (F1 score of 0.77).

CONCLUSIONS

Our study is meaningful in that we developed an early mortality prediction model using only the initial chest X-ray and EHR data of COVID-19 patients. Early prediction of the clinical courses of patients is helpful for not only treatment but also bed management. Our results confirmed the performance improvement of the ensemble model achieved by combining AI models. Through the SHAP method, laboratory tests that indicate the factors affecting COVID-19 mortality were discovered, highlighting the importance of these tests in managing COVID-19 patients.

Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts

This study was designed to develop machine-learning models to predict COVID-19 mortality and identify its key features based on clinical characteristics and laboratory tests. For this, deep-learning (DL) and machine-learning (ML) models were developed using receiver operating characteristic (ROC) area under the curve (AUC) and F1 score optimization of 87 parameters. Of the two, the DL model exhibited better performance (AUC 0.8721, accuracy 0.84, and F1 score 0.76). However, we also blended DL with ML, and the ensemble model performed the best (AUC 0.8811, accuracy 0.85, and F1 score 0.77). The DL model is generally unable to extract feature importance; however, we succeeded by using the Shapley Additive exPlanations method for each model. This study demonstrated both the applicability of DL and ML models for classifying COVID-19 mortality using hospital-structured data and that the ensemble model had the best predictive ability.

Machine learning in medical applications: A review of state-of-the-art methods

Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.

Muscle plays a more superior role than fat in bone homeostasis: A cross-sectional study of old Asian people

OBJECTIVES

The aim of this study was to discover the role of fat and muscle in bone structures, as well as the relationship between obesity and sarcopenia on age-related osteoporosis.

METHODS

A total of 400 participants (65.0 ± 8.2 years old, 42.3% women) were recruited. Fat, muscle, bone parameters, basic demographics, medical history, physical performance and activity, and calcium intake of participants were obtained from datasets. The diagnosis of osteoporosis, sarcopenia, and obesity was based on current recommendations. Pearson correlation, non-linear regression models, and decision tree analyses were performed to study the relationship between fat, muscle, and bone. Logistic regression analyses were used to explore the risk of osteoporosis in old people with obesity or sarcopenia via Model 1 (unadjusted) and Model 2 (adjusted by age, physical activity, and calcium intake).

RESULTS

Correlation analysis showed that limb muscle mass and index, and age were best related to bone mineral density (BMD) (|r| = 0.386-0.632, p < 0.001). On the contrary, body mass index (BMI) and increased body fat percentage (BF%) were harmful for bone health. An increase of BMI and fat mass index slowed the increase of BMD in the spine, while skeletal muscle mass index accelerated the increase. People with sarcopenia had low muscle mass and strength. When separating subjects into sarcopenia and non-sarcopenia status, sarcopenia was independently related to higher risks of osteoporosis in both models (OR > 1, p < 0.05). BMI-defined obesity in Model 1 as well as BF%-defined obesity in both models did not reduce the risk of osteoporosis in both models (p > 0.05). The decision tree classification (85% accuracy) showed that greater body weight and larger lower limb muscle performance were negatively related to osteoporosis, while fat mass and percentage did not play roles in this prediction.

CONCLUSION

Low muscle mass and function were harmful to bone health. Obesity defined by both BMI and BF% had limited protective roles in osteoporosis. The benefits for bone from increased muscle mass and function play a more superior role than increased fat mass in old people. Sarcopenia prevention and treatment instead of controlling obesity should be recommended as an approach to reduce the risks of age-related osteoporosis and fragility fracture for elderly people.

Defining a master curve of abdominal aortic aneurysm growth and its potential utility of clinical management

OBJECTIVE

The maximum diameter measurement of an abdominal aortic aneurysm (AAA), which depends on orthogonal and axial cross-sections or maximally inscribed spheres within the AAA, plays a significant role in the clinical decision making process. This study aims to build a total of 21 morphological parameters from longitudinal CT scans and analyze their correlations. Furthermore, this work explores the existence of a "master curve" of AAA growth, and tests which parameters serve to enhance its predictability for clinical use.

METHODS

106 CT scan images from 25 Korean AAA patients were retrospectively obtained. We subsequently computed morphological parameters, growth rates, and pair-wise correlations, and attempted to enhance the predictability of the growth for high-risk aneurysms using non-linear curve fitting and least-square minimization.

RESULTS

An exponential AAA growth model was fitted to the maximum spherical diameter, as the best representative of the growth among all parameters (r-square: 0.94) and correctly predicted to 15 of 16 validation scans based on a 95% confidence interval. AAA volume expansion rates were highly correlated (r=0.75) with thrombus accumulation rates.

CONCLUSIONS

The exponential growth model using spherical diameter provides useful information about progression of aneurysm size and enables AAA growth rate extrapolation during a given surveillance period.

Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

Substantial studies indicate that autoantibodies to tumor-associated antigens (TAAbs) arise in early stage of lung cancer (LC). However, since single TAAbs as non-invasive biomarkers reveal low diagnostic performances, a panel approach is needed to provide more clues for early detection of LC. In the present research, potential TAAbs were screened in 150 serum samples by focused protein array based on 154 proteins encoded by cancer driver genes. Indirect enzyme-linked immunosorbent assay (ELISA) was used to verify and validate TAAbs in two independent datasets with 1,054 participants (310 in verification cohort, 744 in validation cohort). In both verification and validation cohorts, eight TAAbs were higher in serum of LC patients compared with normal controls. Moreover, diagnostic models were built and evaluated in the training set and the test set of validation cohort by six data mining methods. In contrast to the other five models, the decision tree (DT) model containing seven TAAbs (TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1), built in the training set, yielded the highest diagnostic value with the area under the receiver operating characteristic curve (AUC) of 0.897, the sensitivity of 94.4% and the specificity of 84.9%. The model was further assessed in the test set and exhibited an AUC of 0.838 with the sensitivity of 89.4% and the specificity of 78.2%. Interestingly, the accuracies of this model in both early and advanced stage were close to 90%, much more effective than that of single TAAbs. Protein array based on cancer driver genes is effective in screening and discovering potential TAAbs of LC. The TAAbs panel with TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1 is excellent in early detection of LC, and they might be new target in LC immunotherapy.

Development of machine learning model for diagnostic disease prediction based on laboratory tests

The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.

The Association Between Curvature and Rupture in a Murine Model of Abdominal Aortic Aneurysm and Dissection

BACKGROUND

Mouse models of abdominal aortic aneurysm (AAA) and dissection have proven to be invaluable in the advancement of diagnostics and therapeutics by providing a platform to decipher response variables that are elusive in human populations. One such model involves systemic Angiotensin II (Ang-II) infusion into low density-lipoprotein receptor-deficient (LDLr-/-) mice leading to intramural thrombus formation, inflammation, matrix degradation, dilation, and dissection. Despite its effectiveness, considerable experimental variability has been observed in AAAs taken from our Ang-II infused LDLr-/- mice (n=12) with obvious dissection occurring in 3 samples, outer bulge radii ranging from 0.73 to 2.12 mm, burst pressures ranging from 155 to 540 mmHg, and rupture location occurring 0.05 to 2.53 mm from the peak bulge location.

OBJECTIVE

We hypothesized that surface curvature, a fundamental measure of shape, could serve as a useful predictor of AAA failure at supra-physiological inflation pressures.

METHODS

To test this hypothesis, we fit well-known biquadratic surface patches to 360° micro-mechanical test data and used Spearman's rank correlation (rho) to identify relationships between failure metrics and curvature indices.

RESULTS

We found the strongest associations between burst pressure and the maximum value of the first principal curvature (rho=-0.591, p-val=0.061), the maximum value of Mean curvature (rho=-0.545, p-val=0.087), and local values of Mean curvature at the burst location (rho=-0.864, p-val=0.001) with only the latter significant after Bonferroni correction. Additionally, the surface profile at failure was predominantly convex and hyperbolic (saddle-shaped) as indicated by a negative sign in the Gaussian curvature. Findings reiterate the importance of shape in experimental models of AAA.

Use of Machine Learning for Prediction of Patient Risk of Postoperative Complications After Liver, Pancreatic, and Colorectal Surgery

BACKGROUND

Surgical resection is the only potentially curative treatment for patients with colorectal, liver, and pancreatic cancers. Although these procedures are performed with low mortality, rates of complications remain relatively high following hepatopancreatic and colorectal surgery.

METHODS

The American College of Surgeons (ACS) National Surgical Quality Improvement Program was utilized to identify patients undergoing liver, pancreatic and colorectal surgery from 2014 to 2016. Decision tree models were utilized to predict the occurrence of any complication, as well as specific complications. To assess the variability of the performance of the classification trees, bootstrapping was performed on 50% of the sample.

RESULTS

Algorithms were derived from a total of 15,657 patients who met inclusion criteria. The algorithm had a good predictive ability for the occurrence of any complication, with a C-statistic of 0.74, outperforming the ASA (C-statistic 0.58) and ACS-Surgical Risk Calculator (C-statistic 0.71). The algorithm was able to predict with high accuracy thirteen out of the seventeen complications analyzed. The best performance was in the prediction of stroke (C-statistic 0.98), followed by wound dehiscence, cardiac arrest, and progressive renal failure (all C-statistic 0.96). The algorithm had a good predictive ability for superficial SSI (C-statistic 0.76), organ space SSI (C-statistic 0.76), sepsis (C-statistic 0.79), and bleeding requiring transfusion (C-statistic 0.79).

CONCLUSION

Machine learning was used to develop an algorithm that accurately predicted patient risk of developing complications following liver, pancreatic, or colorectal surgery. The algorithm had very good predictive ability to predict specific complications and demonstrated superiority over other established methods.

A Comparative Study of Biomechanical and Geometrical Attributes of Abdominal Aortic Aneurysms in the Asian and Caucasian Populations

In this work, we provide a quantitative assessment of the biomechanical and geometric features that characterize abdominal aortic aneurysm (AAA) models generated from 19 Asian and 19 Caucasian diameter-matched AAA patients. 3D patient-specific finite element models were generated and used to compute peak wall stress (PWS), 99th percentile wall stress (99th WS), and spatially averaged wall stress (AWS) for each AAA. In addition, 51 global geometric indices were calculated, which quantify the wall thickness, shape, and curvature of each AAA. The indices were correlated with 99th WS (the only biomechanical metric that exhibited significant association with geometric indices) using Spearman's correlation and subsequently with multivariate linear regression using backward elimination. For the Asian AAA group, 99th WS was highly correlated (R2 = 0.77) with three geometric indices, namely tortuosity, intraluminal thrombus volume, and area-averaged Gaussian curvature. Similarly, 99th WS in the Caucasian AAA group was highly correlated (R2 = 0.87) with six geometric indices, namely maximum AAA diameter, distal neck diameter, diameter-height ratio, minimum wall thickness variance, mode of the wall thickness variance, and area-averaged Gaussian curvature. Significant differences were found between the two groups for ten geometric indices; however, no differences were found for any of their respective biomechanical attributes. Assuming maximum AAA diameter as the most predictive metric for wall stress was found to be imprecise: 24% and 28% accuracy for the Asian and Caucasian groups, respectively. This investigation reveals that geometric indices other than maximum AAA diameter can serve as predictors of wall stress, and potentially for assessment of aneurysm rupture risk, in the Asian and Caucasian AAA populations.

A canonical correlation analysis of the relationship between clinical attributes and patient-specific hemodynamic indices in adult pulmonary hypertension

Pulmonary hypertension (PH) is a progressive disease affecting approximately 10-52 cases per million, with a higher incidence in women, and with a high mortality associated with right ventricle (RV) failure. In this work, we explore the relationship between hemodynamic indices, calculated from in silico models of the pulmonary circulation, and clinical attributes of RV workload and pathological traits. Thirty-four patient-specific pulmonary arterial tree geometries were reconstructed from computed tomography angiography images and used for volume meshing for subsequent computational fluid dynamics (CFD) simulations. Data obtained from the CFD simulations were post-processed resulting in hemodynamic indices representative of the blood flow dynamics. A retrospective review of medical records was performed to collect the clinical variables measured or calculated from standard hospital examinations. Statistical analyses and canonical correlation analysis (CCA) were performed for the clinical variables and hemodynamic indices. Systolic pulmonary artery pressure (sPAP), diastolic pulmonary artery pressure (dPAP), cardiac output (CO), and stroke volume (SV) were moderately correlated with spatially averaged wall shear stress (0.60 ≤ R² ≤ 0.66; p < 0.05). Similarly, the CCA revealed a linear and strong relationship (ρ = 0.87; p << 0.001) between 5 clinical variables and 2 hemodynamic indices. To this end, in silico models of PH blood flow dynamics have a high potential for predicting the relevant clinical attributes of PH if analyzed in a group-wise manner using CCA.

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

OBJECTIVE

For 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.

METHODS

106 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.

RESULTS

The 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.

CONCLUSION

A computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth.

A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features

Recent studies monitoring severity of abdominal aortic aneurysm (AAA) suggested that reliance on only the maximum transverse diameter ( D _max ) may be insufficient to predict AAA rupture risk. Moreover, geometric indices, biomechanical parameters, material properties, and patient-specific historical data affect AAA morphology, indicating the need for an integrative approach that incorporates all factors for more accurate estimation of AAA severity. We implemented a machine learning algorithm using 45 features extracted from 66 patients. The model was generated using the J48 decision tree algorithm with the aim of maximizing model accuracy. Three different feature sets were used to assess the prediction rate: i) using D _max as a single-feature set, ii) using a set of all features, and, lastly iii) using a feature set selected via the BestFirst feature selection algorithm. Our results indicate that BestFirst feature selection yielded the highest prediction accuracy. These results indicate that a combination of several specific parameters that comprehensively capture AAA behavior may enable a suitable assessment of AAA severity, suggesting the potential benefit of machine learning for this application.

A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms

The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tomography angiography images of 100 asymptomatic and 50 symptomatic AAA patients who received an elective or emergent repair, respectively, within 1-6 months of their last follow up. An in-house script developed within the MATLAB computational platform was used to segment the clinical images, calculate 53 descriptors of AAA geometry, and generate volume meshes suitable for finite element analysis (FEA). Using a third party FEA solver, four biomechanical markers were calculated from the wall stress distributions. Eight machine learning algorithms (MLA) were used to develop classification models based on the discriminatory potential of the demographic, geometric, and biomechanical variables. The overall classification performance of the algorithms was assessed by the accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and precision of their predictions. The generalized additive model (GAM) was found to have the highest accuracy (87%), AUC (89%), and sensitivity (78%), and the third highest specificity (92%), in classifying the individual AAA as either asymptomatic or symptomatic. The k-nearest neighbor classifier yielded the highest specificity (96%). GAM used seven markers (six geometric and one biomechanical) to develop the classifier. The maximum transverse dimension, the average wall thickness at the maximum diameter, and the spatially averaged wall stress were found to be the most influential markers in the classification analysis. A second classification analysis revealed that using maximum diameter alone results in a lower accuracy (79%) than using GAM with seven geometric and biomechanical markers. We infer from these results that biomechanical and geometric measures by themselves are not sufficient to discriminate adequately between population samples of asymptomatic and symptomatic AAA, whereas MLA offer a statistical approach to stratification of rupture risk by combining demographic, geometric, and biomechanical attributes of patient-specific AAA.

High-frequency murine ultrasound provides enhanced metrics of BAPN-induced AAA growth

An abdominal aortic aneurysm (AAA), defined as a pathological expansion of the largest artery in the abdomen, is a common vascular disease that frequently leads to death if rupture occurs. Once diagnosed, clinicians typically evaluate the rupture risk based on maximum diameter of the aneurysm, a limited metric that is not accurate for all patients. In this study, we worked to evaluate additional distinguishing factors between growing and stable murine aneurysms toward the aim of eventually improving clinical rupture risk assessment. With the use of a relatively new mouse model that combines surgical application of topical elastase to cause initial aortic expansion and a lysyl oxidase inhibitor, β-aminopropionitrile (BAPN), in the drinking water, we were able to create large AAAs that expanded over 28 days. We further sought to develop and demonstrate applications of advanced imaging approaches, including four-dimensional ultrasound (4DUS), to evaluate alternative geometric and biomechanical parameters between 1) growing AAAs, 2) stable AAAs, and 3) nonaneurysmal control mice. Our study confirmed the reproducibility of this murine model and found reduced circumferential strain values, greater tortuosity, and increased elastin degradation in mice with aneurysms. We also found that expanding murine AAAs had increased peak wall stress and surface area per length compared with stable aneurysms. The results from this work provide clear growth patterns associated with BAPN-elastase murine aneurysms and demonstrate the capabilities of high-frequency ultrasound. These data could help lay the groundwork for improving insight into clinical prediction of AAA expansion.NEW & NOTEWORTHY This work characterizes a relatively new murine model of abdominal aortic aneurysms (AAAs) by quantifying vascular strain, stress, and geometry. Furthermore, Green-Lagrange strain was calculated with a novel mapping approach using four-dimensional ultrasound. We also compared growing and stable AAAs, finding peak wall stress and surface area per length to be most indicative of growth. In all AAAs, strain and elastin health declined, whereas tortuosity increased.

Pentagalloyl Glucose and Its Functional Role in Vascular Health: Biomechanics and Drug-Delivery Characteristics

Pentagalloyl glucose (PGG) is an elastin-stabilizing polyphenolic compound that has significant biomedical benefits, such as being a free radical sink, an anti-inflammatory agent, anti-diabetic agent, enzymatic resistant properties, etc. This review article focuses on the important benefits of PGG on vascular health, including its role in tissue mechanics, the different modes of pharmacological administration (e.g., oral, intravenous and endovascular route, intraperitoneal route, subcutaneous route, and nanoparticle based delivery and microbubble-based delivery), and its potential therapeutic role in vascular diseases such as abdominal aortic aneurysms (AAA). In particular, the use of PGG for AAA suppression and prevention has been demonstrated to be effective only in the calcium chloride rat AAA model. Therefore, in this critical review we address the challenges that lie ahead for the clinical translation of PGG as an AAA growth suppressor.