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

Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches

Research on abdominal aortic aneurysms (AAAs) primarily focuses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. Computational fluid dynamics (CFDs), finite element analysis (FEA), and fluid-structure interaction (FSI) are widely used to simulate AAA hemodynamics and biomechanics. However, the accuracy of these simulations depends on the utilization of realistic and sophisticated boundary conditions (BCs), which are essential for properly integrating the AAA with the rest of the cardiovascular system. Recent advances in machine learning (ML) techniques have introduced faster, data-driven surrogates for AAA modeling. These approaches can accelerate segmentation, predict hemodynamics and biomechanics, and assess disease progression. However, their reliability depends on high-quality training data derived from CFDs and FEA simulations, where BC modeling plays a crucial role. Accurate BCs can enhance ML predictions, increasing the clinical applicability. This paper reviews existing BC models, discussing their limitations and technical challenges. Additionally, recent advancements in ML and data-driven techniques are explored, discussing their current states, future directions, common algorithms, and limitations.

Modulation of arterial wall remodeling by mechanical stress: Focus on abdominal aortic aneurysm

The rupture of an abdominal aortic aneurysm (AAA) poses a significant threat, with a high mortality rate, and the mechanical stability of the arterial wall determines both its growth and potential for rupture. Owing to extracellular matrix (ECM) degradation, wall-resident cells are subjected to an aberrant mechanical stress environment. In response to stress, the cellular mechanical signaling pathway is activated, initiating the remodeling of the arterial wall to restore stability. A decline in mechanical signal responsiveness, coupled with inadequate remodeling, significantly contributes to the AAA's progressive expansion and eventual rupture. In this review, we summarize the main stresses experienced by the arterial wall, emphasizing the critical role of the ECM in withstanding stress and the importance of stress-exposed cells in maintaining mechanical stability. Furthermore, we will discuss the application of biomechanical analyses as a predictive tool for assessing AAA stability.

Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm

OBJECTIVE

The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, peri-procedural death, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of this study was to construct and validate a random forest machine learning tool to predict two year survival following elective AAA repair.

METHODS

All patients who underwent elective open or endovascular repair of AAA from 1 January 2008 to 31 March 2021 were reviewed. They were assessed using the Vascular Services Quality Improvement Program pathway involving cardiopulmonary exercise testing, contrast enhanced computerised tomography scan, and multidisciplinary assessment. Patients were followed up for at least two years. A RFC was developed using 70% of the dataset and validated using 30% to predict survival for at least two years following AAA repair.

RESULTS

Nine hundred and twenty five patients (n = 836 men; n = 89 women) underwent elective AAA repair; 126 (13.6%) died during the first two years; 11 (1.2%) died peri-procedurally. Variable importance analysis suggested that anaerobic threshold, pre-operative haemoglobin, maximal O2 consumption, body mass index, risk category, and forced expiratory volume in 1 second - forced vital capacity ratio were the most important contributors to the model. Sensitivity and specificity of the RFC for prediction of two year survival following surgery was 96.7% (95% CI 94.4 - 99%) and 67.1% (95% CI 61 - 72%); overall accuracy: 92.6% (95% CI 88 - 95%) (positive predictive value: 0.93, negative predictive value: 0.80); 10 fold cross validation revealed area under the receiver operator characteristic curve of 0.88.

CONCLUSION

RFCs based on readily available clinical data can successfully predict survival in the first two years following elective AAA repair. Such information can contribute to the risk benefit assessment when deciding to electively repair AAAs.

Temporal geometric mapping defines morphoelastic growth model of Type B aortic dissection evolution

The human aorta undergoes complex morphologic changes that mirror the evolution of disease. Finite element analysis (FEA) enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into FEA to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing that pathologic growth drives morphologic heterogeneity. FEA-derived trajectories in a shape-size geometric feature space, which plots the variance of the shape index versus the inverse square root of aortic surface area (δS vs. [Formula: see text] ), quantitatively demonstrate an increase in δS. This represents a deviation from physiologic shape changes and parallels the true geometric progression of aortic disease patients.

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials

BACKGROUND AND OBJECTIVE

Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed.

METHODS

The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized.

RESULTS

ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determinationR 2 . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models.

CONCLUSION

ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve highR 2 values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution

The human aorta undergoes complex morphologic changes that indicate the evolution of disease. Finite element analysis enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into finite element analysis (FEA) to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing pathologic growth drives morphologic heterogeneity. A shape-size geometric feature space plotting the variance of the shape index versus the inverse square root of aortic surface area (δ𝒮 vs. ) quantitatively demonstrates the simulated breakdown in aortic shape. An increase in δ𝒮 closely parallels the true geometric progression of aortic disease patients.

A dedicated risk prediction model of 1-year mortality following endovascular aortic aneurysm repair involving the renal-mesenteric arteries

OBJECTIVE

Treatment goals of prophylactic endovascular aortic repair of complex aneurysms involving the renal-mesenteric arteries (complex endovascular aortic repair [cEVAR]) include achieving both technical success and long-term survival benefit. Mortality within the first year after cEVAR likely indicates treatment failure owing to associated costs and procedural complexity. Notably, no validated clinical decision aid tools exist that reliably predict mortality after cEVAR. The purpose of this study was to derive and validate a preoperative prediction model of 1-year mortality after elective cEVAR.

METHODS

All elective cEVARs including fenestrated, branched, and/or chimney procedures for aortic disease extent confined proximally to Ishimaru landing zones 6 to 9 in the Society for Vascular Surgery Vascular Quality Initiative were identified (January 2012 to August 2023). Patients (n = 4053) were randomly divided into training (n = 3039) and validation (n = 1014) datasets. A logistic regression model for 1-year mortality was created and internally validated by bootstrapping the AUC and calibration intercept and slope, and by using the model to predict 1-year mortality in the validation dataset. Independent predictors were assigned an integer score, based on model beta-coefficients, to generate a simplified scoring system to categorize patient risk.

RESULTS

The overall crude 1-year mortality rate after elective cEVAR was 11.3% (n = 456/4053). Independent preoperative predictors of 1-year mortality included chronic obstructive pulmonary disease, chronic renal insufficiency (creatinine >1.8 mg/dL or dialysis dependence), hemoglobin <12 g/dL, decreasing body mass index, congestive heart failure, increasing age, American Society of Anesthesiologists class ≥IV, current tobacco use, history of peripheral vascular intervention, and increasing extent of aortic disease. The 1-year mortality rate varied from 4% among the 23% of patients classified as low risk to 23% for the 24% classified as high risk. Performance of the model in validation was comparable with performance in the training data. The internally validated scoring system classified patients roughly into quartiles of risk (low, low/medium, medium/high and high), with 52% of patients categorized as medium/high to high risk, which had corresponding 1-year mortality rates of 11% and 23%, respectively. Aneurysm diameter was below Society for Vascular Surgery recommended treatment thresholds (<5.0 cm in females, <5.5 cm in males) in 17% of patients (n = 679/3961), 41% of whom were categorized as medium/high or high risk. This subgroup had significantly increased in-hospital complication rates (18% vs 12%; P = .02) and 1-year mortality (13% vs 5%; P < .0001) compared with patients in the low- or low/medium-risk groups with guideline-compliant aneurysm diameters (≥5.0 cm in females, ≥5.5 cm in males).

CONCLUSIONS

This validated preoperative prediction model for 1-year mortality after cEVAR incorporates physiological, functional, and anatomical variables. This novel and simplified scoring system can effectively discriminate mortality risk and, when applied prospectively, may facilitate improved preoperative decision-making, complex aneurysm care delivery, and resource allocation.

An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes

Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their clinically-estimated risk of rupture-an event that is the 13th leading cause of death in the US-exceeds the risk associated with repair. Yet the current clinical guideline remains a one-size-fits-all "maximum diameter criterion" whereby AAA exceeding a threshold diameter is thought to make the risk of rupture high enough to warrant intervention. However, between 7 and 23.4% of smaller-sized AAA have been reported to rupture with diameters below the threshold. In this study, we train and assess machine learning models using clinical, biomechanical, and morphological indices from 381 patients to develop an aneurysm prognosis classifier to predict one of three outcomes for a given AAA patient: their AAA will remain stable, their AAA will require repair based as currently indicated from the maximum diameter criterion, or their AAA will rupture. This study represents the largest cohort of AAA patients that utilizes the first available medical image and clinical data to classify patient outcomes. The APC model therefore represents a potential clinical tool to striate specific patient outcomes using machine learning models and patient-specific image-based (biomechanical and morphological) and clinical data as input. Such a tool could greatly assist clinicians in their management decisions for patients with AAA.

A radiomics model for predicting the outcome of endovascular abdominal aortic aneurysm repair based on machine learning

OBJECTIVE

This study aimed to develop a radiomics model to predict the outcome of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA), based on machine learning (ML) algorithms.

METHODS

We retrospectively reviewed 711 patients with infra-renal AAA who underwent elective EVAR procedures between January 2016 and December 2019 at our single center. The radiomics features of AAA were extracted using Pyradiomics. Pearson correlation analysis, analysis of variance (ANOVA), least absolute shrinkage, and selection operator (LASSO) regression were applied to determine the predictors for EVAR-related severe adverse events (SAEs). Eighty percent of patients were classified as the training set and the remaining 20 percent of patients were classified as the test set. The selected features were used to build a radiomics model in training set using different ML algorithms. The performance of each model was assessed using the area under the curve (AUC) from the receiver operating characteristic (ROC) curve in the test set.

RESULTS

A total of 493 patients were enrolled in this study, the mean follow-up time was 32 months. During the follow-up, 156 (31.6%) patients experienced EVAR-related SAEs. A total of 1223 radiomics features were extracted from each patient, of which 30 radiomics features were finally identified. The quantitative performance assessment and the ROC curves indicated that the logistics regression (LR) model had better predictive value than others, with accuracy, 0.86; AUC, 0.93; and F1 score, 0.91. The Rad-score waterfall plot showed that the overall amount of error was small both in the training set and in the test set. Calibration curve showed that the calibration degree of the training set and the test set were good (p > 0.05). Decision curve analysis (threshold 0.32) demonstrated that the model had good clinical applicability.

CONCLUSION

Our radiomics model could be used as an efficient and adjunctive tool to predict the outcome after EVAR.

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.

Machine learning model predicts the occurrence of acute kidney injury after open surgery for abdominal aortic aneurysm repair

OBJECTIVES

Abdominal aortic aneurysm is a pathological condition in which the abdominal aorta is dilated beyond 3.0 cm. The surgical options include open surgical repair (OSR) and endovascular aneurysm repair (EVAR). Prediction of acute kidney injury (AKI) after OSR is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, this study aims to perform tests on the efficacy of different machine learning models.

METHODS

Perioperative data of 80 OSR patients were retrospectively collected from January 2009 to December 2021 at Xiangya Hospital, Central South University. The vascular surgeon performed the surgical operation. Four commonly used machine learning classification models (logistic regression, linear kernel support vector machine, Gaussian kernel support vector machine, and random forest) were chosen to predict AKI. The efficacy of the models was validated by five-fold cross-validation.

RESULTS

AKI was identified in 33 patients. Five-fold cross-validation showed that among the 4 classification models, random forest was the most precise model for predicting AKI, with an area under the curve of 0.90±0.12.

CONCLUSIONS

Machine learning models can precisely predict AKI during early stages after surgery, which allows vascular surgeons to address complications earlier and may help improve the clinical outcomes of OSR.

Prediction of abdominal aortic aneurysm growth by artificial intelligence taking into account clinical, biologic, morphologic, and biomechanical variables

OBJECTIVES

To develop a prediction model that could risk stratify abdominal aortic aneurysms (AAAs) into high and low growth rate groups, using machine learning algorithms based on variables from different pathophysiological fields.

METHODS

A cohort of 40 patients with small AAAs (maximum diameter 32-53 mm) who had at least an initial and a follow-up CT scan (median follow-up 12 months, range 3-36 months) were included. 29 input variables from clinical, biological, morphometric, and biomechanical pathophysiological aspects extracted for predictive modeling. Collected data were used to build two supervised machine learning models. A gradient boosting (XGboost) and a support vector machines (SVM) algorithm were trained with 60% and tested with 40% of the data to predict which AAA would achieve a growth rate higher than the median of our study cohort. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used for the evaluation of the developed algorithms.

RESULTS

XGboost achieved the highest AUC in predicting high compared to low AAA growth rate with an AUC of 81.2% (95% CI from 61.1 to 100%). SVM achieved the second highest performance with an AUC of 68.8% (95% CI from 46.5 to 91%). Based on the best performing algorithm, variable importance was estimated. Diameter-diameter ratio (maximum diameter/neck diameter), Tortuosity from Renal arteries to aortic bifurcation, and maximum thickness of the intraluminal thrombus were found to be the most important factors for model predictions. Other factors were also found to play a significant but less important role.

CONCLUSIONS

A prediction model that can risk stratify AAAs into high and low growth rate groups could be developed by analyzing several factors implicated in the multifactorial pathophysiology of this disease, with the use of machine learning algorithms. Future studies including larger patient cohorts and implementing additional risk markers may aid in the establishment of such methodology during AAA rupture risk estimation.

Artificial intelligence and machine learning in aortic disease

PURPOSE OF REVIEW

Discuss foundational concepts for artificial intelligence (AI) and review recent literature on its application to aortic disease.

RECENT FINDINGS

Machine learning (ML) techniques are rapidly evolving for the evaluation of aortic disease - broadly categorized as algorithms for aortic segmentation, detection of pathology, and risk stratification. Advances in deep learning, particularly U-Net architectures, have revolutionized segmentation of the aorta and show potential for monitoring the size of aortic aneurysm and characterizing aortic dissection. These algorithms also facilitate application of more complex technologies including analysis of flow dynamics with 4D Flow magnetic resonance imaging (MRI) and computational simulation of fluid dynamics for aortic coarctation. In addition, AI algorithms have been proposed to assist in 'opportunistic' screening from routine imaging exams, including automated aortic calcification score, which has emerged as a strong predictor of cardiovascular risk. Finally, several ML algorithms are being explored for risk stratification of patients with aortic aneurysm and dissection, in addition to prediction of postprocedural complications.

SUMMARY

Multiple ML techniques have potential for characterization and risk prediction of aortic aneurysm, dissection, coarctation, and atherosclerotic disease on computed tomography and MRI. This nascent field shows considerable promise with many applications in development and in early preclinical evaluation.

Efficient parallel simulation of hemodynamics in patient-specific abdominal aorta with aneurysm

Surgical planning for aortic aneurysm repair is a difficult task. In addition to the morphological features obtained from medical imaging, alternative features obtained with computational modeling may provide additional useful information. Though numerical studies are noninvasive, they are often time-consuming, especially when we need to study and compare multiple repair scenarios, because of the high computational complexity. In this paper, we present a highly parallel algorithm for the numerical simulation of unsteady blood flows in the patient-specific abdominal aorta before and after the aneurysmic repair. We model the blood flow with the unsteady incompressible Navier-Stokes equations with different outlet boundary conditions, and solve the discretized system with a highly scalable domain decomposition method. With this approach, a high resolution simulation of a full-size adult aorta can be obtained in less than an hour, instead of days with older methods and software. In addition, we show that the parallel efficiency of the proposed method is near 70% on a parallel computer with 2, 880 processor cores.

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.