A Comparative Study of Machine Learning and Algorithmic Approaches to Automatically Identify the Yield Point in Normal and Aneurysmal Human Aortic Tissues.

The stress-strain curve of biological soft tissues helps characterize their mechanical behavior. The yield point on this curve is when a specimen breaches its elastic range due to irreversible microstructural damage. The yield point is easily found using the offset yield method in traditional engineering materials. However, correctly identifying the yield point in soft tissues can be subjective due to its nonlinear material behavior. The typical method for yield point identification is visual inspection, which is investigator-dependent and does not lend itself to automation of the analysis pipeline. An automated algorithm to identify the yield point objectively assesses soft tissues' biomechanical properties. This study aimed to analyze data from uniaxial extension testing on biological soft tissue specimens and create a machine learning (ML) model to determine a tissue sample's yield point. We present a trained machine learning model from 279 uniaxial extension curves from testing aneurysmal/nonaneurysmal and longitudinal/circumferential oriented tissue specimens that multiple experts labeled through an adjudication process. The ML model showed a median error of 5% in its estimated yield stress compared to the expert picks. The study found that an ML model could accurately identify the yield point (as defined) in various aortic tissues. Future studies will be performed to validate this approach by visually inspecting when damage occurs and adjusting the model using the ML-based approach.

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.

Mechanical and matrix effects of short and long-duration exposure to beta-aminopropionitrile in elastase-induced model abdominal aortic aneurysm in mice.

Objective: Evaluate the mechanical and matrix effects on abdominal aortic aneurysms (AAA) during the initial aortic dilation and after prolonged exposure to beta-aminopropionitrile (BAPN) in a topical elastase AAA model. Methods: Abdominal aortae of C57/BL6 mice were exposed to topical elastase with or without BAPN in the drinking water starting 4 days before elastase exposure. For the standard AAA model, animals were harvested at 2 weeks after active elastase (STD2) or heat-inactivated elastase (SHAM2). For the enhanced elastase model, BAPN treatment continued for either 4 days (ENH2b) or until harvest (ENH2) at 2 weeks; BAPN was continued until harvest at 8 weeks in one group (ENH8). Each group underwent assessment of aortic diameter, mechanical testing (tangent modulus and ultimate tensile strength [UTS]), and quantification of insoluble elastin and bulk collagen in both the elastase exposed aorta as well as the descending thoracic aorta. Results: BAPN treatment did not increase aortic dilation compared with the standard model after 2 weeks (ENH2, 1.65 ± 0.23 mm; ENH2b, 1.49 ± 0.39 mm; STD2, 1.67 ± 0.29 mm; and SHAM2, 0.73 ± 0.10 mm), but did result in increased dilation after 8 weeks (4.3 ± 2.0 mm; P = .005). After 2 weeks, compared with the standard model, continuous therapy with BAPN did not have an effect on UTS (24.84 ± 7.62 N/cm2; 18.05 ± 4.95 N/cm2), tangent modulus (32.60 ± 9.83 N/cm2; 26.13 ± 9.10 N/cm2), elastin (7.41 ± 2.43%; 7.37 ± 4.00%), or collagen (4.25 ± 0.79%; 5.86 ± 1.19%) content. The brief treatment, EHN2b, resulted in increased aortic collagen content compared with STD2 (7.55 ± 2.48%; P = .006) and an increase in UTS compared with ENH2 (35.18 ± 18.60 N/cm2; P = .03). The ENH8 group had the lowest tangent modulus (3.71 ± 3.10 N/cm2; P = .005) compared with all aortas harvested at 2 weeks and a lower UTS (2.18 ± 2.18 N/cm2) compared with both the STD2 (24.84 ± 7.62 N/cm2; P = .008) and ENH2b (35.18 ± 18.60 N/cm2; P = .001) groups. No differences in the mechanical properties or matrix protein concentrations were associated with abdominal elastase exposure or BAPN treatment for the thoracic aorta. The tangent modulus was higher in the STD2 group (32.60 ± 9.83 N/cm2; P = .0456) vs the SHAM2 group (17.99 ± 5.76 N/cm2), and the UTS was lower in the ENH2 group (18.05 ± 4.95 N/cm2; P = .0292) compared with the ENH2b group (35.18 ± 18.60 N/cm2). The ENH8 group had the lowest tangent modulus (3.71 ± 3.10 N/cm2; P = .005) compared with all aortas harvested at 2 weeks and a lower UTS (2.18 ± 2.18 N/cm2) compared with both the STD2 (24.84 ± 7.62 N/cm2; P = .008) and ENH2b (35.18 ± 18.60 N/cm2; P = .001) groups. Abdominal aortic elastin in the STD2 group (7.41 ± 2.43%; P = .035) was lower compared with the SHAM2 group (15.29 ± 7.66%). Aortic collagen was lower in the STD2 group (4.25 ± 0.79%; P = .007) compared with the SHAM2 group (12.44 ± 6.02%) and higher for the ENH2b (7.55 ± 2.48%; P = .006) compared with the STD2 group. Conclusions: Enhancing an elastase AAA model with BAPN does not affect the initial (2-week) dilation phase substantially, either mechanically or by altering the matrix content. Late mechanical and matrix effects of prolonged BAPN treatment are limited to the elastase-exposed segment of the aorta. Clinical Relevance: This paper explores the use of short- and long-term exposure to beta-aminopropionitrile to create an enhanced topical elastase abdominal aortic aneurysm model in mice. Readouts of aneurysm severity included loss of mechanical stability and vascular extracellular matrix composition reminiscent of what is seen in the course of human disease. Additionally, we show that the thoracic aorta, unlike the findings below the renal arteries, is not damaged in our animal model.

An Objective and Repeatable Sac Isolation Technique for Comparing Biomechanical Metrics in Abdominal Aortic Aneurysms.

(1) Abdominal aortic aneurysm (AAA) biomechanics-based metrics often reported may be over/under-estimated by including non-aneurysmal regions in the analyses, which is typical, rather than isolating the dilated sac region. We demonstrate the utility of a novel sac-isolation algorithm by comparing peak/mean wall stress (PWS, MWS), with/without sac isolation, for AAA that were categorized as stable or unstable in 245 patient CT image sets. (2) 245 patient computed tomography images were collected, segmented, meshed, and had subsequent finite element analysis performed in preparation of our novel sac isolation technique. Sac isolation was initiated by rotating 3D surfaces incrementally, extracting 2D projections, curve fitting a Fourier series, and taking the local extrema as superior/inferior boundaries for the aneurysmal sac. The PWS/MWS were compared pairwise using the entire aneurysm and the isolated sac alone. (3) MWS, not PWS, was significantly different between the sac alone and the entire aneurysm. We found no statistically significant difference in wall stress measures between stable (n = 222) and unstable (n = 23) groups using the entire aneurysm. However, using sac-isolation, PWS (24.6 ± 7.06 vs. 20.5 ± 8.04 N/cm2; p = 0.003) and MWS (12.0 ± 3.63 vs. 10.5 ± 4.11 N/cm2; p = 0.022) were both significantly higher in unstable vs. stable groups. (4) Our results suggest that evaluating only the AAA sac can influence wall stress metrics and may reveal differences in stable and unstable groups of aneurysms that may not otherwise be detected when the entire aneurysm is used.