Validation of a Novel Software Tool for Automatic Aortic Annular Sizing in Three-Dimensional Transesophageal Echocardiographic Images

ACR Appropriateness Criteria® Preprocedural Planning for Transcatheter Aortic Valve Replacement: 2023 Update

This document discusses preprocedural planning for transcatheter aortic valve replacement, evaluating the imaging modalities used in initial imaging for preprocedure planning under two variants 1) Preintervention planning for transcatheter aortic valve replacement: assessment of aortic root; and 2) Preintervention planning for transcatheter aortic valve replacement: assessment of supravalvular aorta and vascular access. US echocardiography transesophageal, MRI heart function and morphology without and with IV contrast, MRI heart function and morphology without IV contrast and CT heart function and morphology with IV contrast are usually appropriate for assessment of aortic root. CTA chest with IV contrast, CTA abdomen and pelvis with IV contrast, CTA chest abdomen pelvis with IV contrast are usually appropriate for assessment of supravalvular aorta and vascular access. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Automated and semi-automated 3D echocardiographic software for aortic annulus sizing in transcatheter aortic valve implantation helps bridge the gap between expert and novice operators

3D-transesophageal echocardiography (3D-TEE) is an alternative to multidetector row computed tomography (MDCT) for aortic annulus (AoA) sizing in preparation for Transcatheter aortic valve implantation (TAVI). We aim to evaluate how the fully automated (auto) and semi-automated (SA) TEE methods perform compared to conventional manual TEE method and the gold standard MDCT for annulus sizing both in expert and novice operators. In this prospective cohort study, eighty-nine patients with severe aortic stenosis underwent multimodality imaging with 3D-TEE and MDCT. Annular measurements were collected by expert echocardiographers using 3D auto, SA and manual methods and compared to MDCT. A novice in the field of echocardiography retrospectively measured the AoA for all patients using the same methods. TEE measurements, independently of the method used, had good to very good agreement to MDCT. They significantly underestimated aortic annular area and circumference vs. MDCT with the auto method underestimating it the most and the manual method the least (6.5% and 1.3% respectively for area and circumference). For experts, the manual TEE method offered the least systematic bias while the SA method had narrower limits of agreement (LOA). For the novice operator, SA method provided the least bias and narrower LOA vs. MDCT. There is good agreement between novice and experts for all 3 TEE methods but better agreement with auto and SA methods as opposed to manual one. Our study supports the use of 3D-TEE as a complementary method to MDCT for aortic annular sizing. The newer auto and SA software, that requires minimal operator intervention, is an easy to use, reliable and reproducible tool for aortic annulus sizing for experienced operators, and especially less experienced ones.

Pre-TAVI aortic annulus sizing: comparison between manual and semi-automated new generation software measurements in operators with different experience

OBJECTIVES

Aim of the study is to compare manual and semi-automatic measurements for aortic annulus assessment among different operators.

METHODS

Eighty patients who underwent TAVI were retrospectively enrolled. The measurements manually performed by an experienced reader for aortic annulus (minimum and maximum diameters, perimeter, area), annulus-to-coronary ostia distance and time needed for the whole evaluation, were collected. The same operator (observer 1) and two less experienced readers (observer 2 and 3, with >5 years and 1 year of experience, respectively) assessed the same measurements using a semi-automatic software. Differences between manual and semi-automatic measurements, reading time and suggested valves size derived by CT were compared.

RESULTS

Very good correlations were found between manual and software-aided measurements for aortic annulus area and perimeter in comparison with standard measurements for the three readers (ICC range 0.81-0.98). Good correlations were found for the distance with coronary ostia(0.75-0.79). The same area-derived prosthesis size for manual and semi-automatic measurements was selected in 96% of cases for observer 1; very good correlations were also found for observer 2 and 3 (ICC = 0.89 and 0.88, respectively). Using semi-automatic measurements, the mean time needed for CT images was significantly lower for observers 1 and 2 (1.50 and 1.72versus 3.14 min), respectively.

CONCLUSIONS

Pre-TAVI CT using semi-automatic software allows accurate and reproducible measurements, reducing reconstruction time up to 50% and is reliable even for operators with different experience.

ADVANCES IN KNOWLEDGE

The use of semi-automatic dedicated software for CT in TAVI planning is reliable even for operators without long time experience and allows accurate and reproducible measurements improving pre-TAVI workflow.

Real-time 3D echocardiographic transilluminated imaging combined with artificially intelligent left atrial appendage measurement for atrial fibrillation interventional procedures

Aims: This study investigated the feasibility and accuracy of real-time three-dimensional (3D) echocardiographic transilluminated imaging (TrueVue Glass) in left atrial appendage (LAA) anatomical morphology and artificial intelligence (AI)-assisted 3D automated LAA measurement (3D Auto LAA) software in the preoperative evaluation of LAA occlusion (LAAO) in patients with atrial fibrillation (AF).

Method and results: Thirty-seven patients with AF were selected. Two-dimensional (2D) and real-time 3D transesophageal echocardiography (RT3D-TEE) were performed preoperatively, using conventional 3D, the new 3D TrueVue Glass mode, and cardiac computed tomography angiography (CCTA) to assess and type the morphology of LAA. Physiological parameters were measured using traditional 2D and 3D manual (3D Manual LAA), 3D Auto LAA, and CCTA. TrueVue Glass for LAA outer contour display was compared with CCTA. Comparisons were based on correlation and consistency in measuring the maximum diameter (LZ max), minimum diameter (LZ min), area (LZ area), and circumference (LZ cir) of LAA landing zone (LZ). Times and variabilities were compared. The concordance rate for external shape of LAA was 97.14% between TrueVue Glass and CCTA. 3D Auto LAA and 3D Manual LAA have a stronger correlation and higher consistency in all parameters. 3D Auto LAA showed higher intra- and interobserver reproducibility and allowed quicker analysis (p < 0.05). LAAO was performed in 35 patients (94.59%), and none of which had serious adverse events.

Conclusion: TrueVue Glass is the first non-invasive and radiation-free visualization of the overall external contour of LAA and its adjacent structures. 3D Auto LAA simplifies the measurement, making the preoperative assessment more efficient and convenient while ensuring the accuracy and reproducibility. A combination of the two is feasible for accurate and rapid assessment of LAA anatomy and physiology in AF patients and has practical application in LAAO.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Dynamic Volumetric Assessment of the Aortic Root: The Influence of Bicuspid Aortic Valve Competence

BACKGROUND

Aortic root evaluation is conventionally based on 2-dimensional measurements at a single phase of the cardiac cycle. This work presents an image analysis method for assessing dynamic 3-dimensional changes in the aortic root of minimally calcified bicuspid aortic valves (BAVs) with and without moderate to severe aortic regurgitation.

METHODS

The aortic root was segmented over the full cardiac cycle in 3-dimensional transesophageal echocardiographic images acquired from 19 patients with minimally calcified BAVs and from 16 patients with physiologically normal tricuspid aortic valves (TAVs). The size and dynamics of the aortic root were assessed using the following image-derived measurements: absolute mean root volume and mean area at the level of the ventriculoaortic junction, sinuses of Valsalva, and sinotubular junction, as well as normalized root volume change and normalized area change of the ventriculoaortic junction, sinuses of Valsalva, and sinotubular junction over the cardiac cycle.

RESULTS

Normalized volume change over the cardiac cycle was significantly greater in BAV roots with moderate to severe regurgitation than in normal TAV roots and in BAV roots with no or mild regurgitation. Aortic root dynamics were most significantly different at the mid-level of the sinuses of Valsalva in BAVs with moderate to severe regurgitation than in competent TAVs and BAVs.

CONCLUSIONS

Echocardiographic reconstruction of the aortic root demonstrates significant differences in dynamics of BAV roots with moderate to severe regurgitation relative to physiologically normal TAVs and competent BAVs. This finding may have implications for risk of future dilatation, dissection, or rupture, which warrant further investigation.

Feasibility and Accuracy of Automated Three-Dimensional Echocardiographic Analysis of Left Atrial Appendage for Transcatheter Closure

BACKGROUND

Procedural success of transcatheter left atrial appendage closure (LAAC) is dependent on correct device selection. Three-dimensional (3D) transesophageal echocardiography (TEE) is more accurate than the two-dimensional modality for evaluation of the complex anatomy of the left atrial appendage (LAA). However, 3D transesophageal echocardiographic analysis of the LAA is challenging and highly expertise dependent. The aim of this study was to evaluate the feasibility and accuracy of a novel software tool for automated 3D analysis of the LAA using 3D transesophageal echocardiographic data.

METHODS

Intraprocedural 3D transesophageal echocardiographic data from 158 patients who underwent LAAC were retrospectively analyzed using a novel automated LAA analysis software tool. On the basis of the 3D transesophageal echocardiographic data, the software semiautomatically segmented the 3D LAA structure, determined the device landing zone, and generated measurements of the landing zone dimensions and LAA length, allowing manual editing if necessary. The accuracy of LAA preimplantation anatomic measurement reproducibility and time for analysis of the automated software were compared against expert manual 3D analysis. The software feasibility to predict the optimal device size was directly compared with implanted models.

RESULTS

Automated 3D analysis of the LAA on 3D TEE was feasible in all patients. There was excellent agreement between automated and manual measurements of landing zone maximal diameter (bias, -0.32; limits of agreement, -3.56 to 2.92), area-derived mean diameter (bias, -0.24; limits of agreement, -3.12 to 2.64), and LAA depth (bias, 0.02; limits of agreement, -3.14 to 3.18). Automated 3D analysis, with manual editing if necessary, accurately identified the implanted device size in 90.5% of patients, outperforming two-dimensional TEE (68.9%; P < .01). The automated software showed results competitive against the manual analysis of 3D TEE, with higher intra- and interobserver reproducibility, and allowed quicker analysis (101.9 ± 9.3 vs 183.5 ± 42.7 sec, P < .001) compared with manual analysis.

CONCLUSIONS

Automated LAA analysis on the basis of 3D TEE is feasible and allows accurate, reproducible, and rapid device sizing decision for LAAC.

Aortic Annular Sizing Using Novel Software in Three-Dimensional Transesophageal Echocardiography for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis

(1) Background: We performed this study to evaluate the agreement between novel automated software of three-dimensional transesophageal echocardiography (3D-TEE) and multidetector computed tomography (MDCT) for aortic annular measurements of preprocedural transcatheter aortic valve replacement (TAVR); (2) Methods: PubMed, EMBASE, Web of Science, and Cochrane Library (Wiley) databases were systematically searched for studies that compared 3D-TEE and MDCT as the reference standard for aortic annular measurement of the following parameters: annular area, annular perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter. Meta-analytic methods were utilized to determine the pooled correlations and mean differences between 3D-TEE and MDCT. Heterogeneity and publication bias were also assessed. Meta-regression analyses were performed based on the potential factors affecting the correlation of aortic annular area; (3) Results: A total of 889 patients from 10 studies were included in the meta-analysis. Pooled correlation coefficients between 3D-TEE and MDCT of annulus area, perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter measurements were strong 0.89 (95% CI: 0.84–0.92), 0.88 (95% CI: 0.83–0.92), 0.87 (95% CI: 0.77–0.93), 0.87 (95% CI: 0.77–0.93), 0.79 (95% CI: 0.64–0.87), and 0.75 (95% CI: 0.61–0.84) (Overall p < 0.0001), respectively. Pooled mean differences between 3D-TEE and MDCT of annulus area, perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter measurements were −20.01 mm² ((95% CI: −35.37 to −0.64), p = 0.011), −2.31 mm ((95% CI: −3.31 to −1.31), p < 0.0001), −0.22 mm ((95% CI: −0.73 to 0.29), p = 0.40), −0.47 mm ((95% CI: −1.06 to 0.12), p = 0.12), −1.36 mm ((95% CI: −2.43 to −0.30), p = 0.012), and 0.31 mm ((95% CI: −0.15 to 0.77), p = 0.18), respectively. There were no statistically significant associations with the baseline patient characteristics of sex, age, left ventricular ejection fraction, mean transaortic gradient, and aortic valve area to the correlation between 3D-TEE and MDCT for aortic annular area sizing; (4) Conclusions: The present study implies that 3D-TEE using novel software tools, automatically analysis, is feasible to MDCT for annulus sizing in clinical practice.

Three-Dimensional Transesophageal Echocardiography as an Alternative to Multidetector Computed Tomography in Aortic Annular Diameter Measurements for Transcatheter Aortic Valve Implantation

Simple Summary

Patients who have multiple associated comorbidities and need to change the aortic valve may have a contraindication to open-heart surgery, the alternative being transcatheter aortic valve implantation, which requires very precise measurements of the aortic annulus to determine the dimensions of the prostheses. Ultrasonographic imaging techniques, such as transesophageal echocardiography, are constantly evolving. The aim of our study was to compare the three-dimensional transesophageal echocardiography and multi-detector computer tomography methods, with the former being an alternative for patients who cannot undergo computer tomography because of a major contraindication. We have demonstrated that there were small differences between aortic annular measurements using multi-detector computer tomography (2.25 ± 0.19 cm) and three-dimensional transesophageal echocardiography (2.25 ± 0.15 cm). Thus, three-dimensional transesophageal echocardiography can be the solution for aortic annular measurements used to select the correct prosthesis for the transcatheter aortic valve implantation procedure in patients who cannot undergo computer tomography.

Abstract

Background and objectives: Transcatheter aortic valve implantation (TAVI) is a therapeutic choice for high surgical risk patients, serving as an alternative to open-heart surgery. Correct measurement of the aortic annulus, which leads to the selection of a suitable prosthesis and accurate outcome prediction, is essential for the success of TAVI. The objective of this study is to evaluate the accuracy of novel imaging te chniques in measuring the aortic annulus by comparing multi-detector computer tomography (MDCT) and three-dimensional transesophageal echocardiography (3D TEE) for the selection of the optimal prosthesis. Materials and Methods: Measurements of the aortic annulus have been performed on 25 patients using MDCT and TEE, and the correlation and agreement levels between the two measuring techniques were analyzed. MDCT measurements were used for the sizing of the prostheses. Results: MDCT and TEE measurements of aortic annular diameters were significantly correlated, with a mean difference of 0.001 cm. Conclusions: 3D TEE measurements have been in good agreement with MDCT and, therefore, 3D TEE can be used as an alternative in cases where MDCT is contraindicated or not available.

Semiautomatic Estimation of Device Size for Left Atrial Appendage Occlusion in 3-D TEE Images

Left atrial appendage (LAA) occlusion is used to reduce the risk of thromboembolism in patients with nonvalvular atrial fibrillation by obstructing the LAA through a percutaneously delivered device. Nonetheless, correct device sizing is complex, requiring the manual estimation of different measurements in preprocedural/periprocedural images, which is tedious and time-consuming and with high interobserver and intraobserver variability. In this paper, a semiautomatic solution to estimate the required relevant clinical measurements is described. This solution starts with the 3-D segmentation of the LAA in 3-D transesophageal echocardiographic images, using a constant blind-ended model initialized through a manually defined spline. Then, the segmented LAA surface is aligned with a set of templates, i.e., 3-D surfaces plus relevant measurement planes (manually defined by one observer), transferring the latter to the unknown situation. Specifically, the alignment is performed in three consecutive steps, namely: 1) rigid alignment using the LAA clipping plane position; 2) orientation compensation using the circumflex artery location; and 3) anatomical refinement through a weighted iterative closest point algorithm. The novel solution was evaluated in a clinical database with 20 volumetric TEE images. Two experiments were set up to assess: 1) the sensitivity of the model's parameters and 2) the accuracy of the proposed solution for the estimation of the clinical measurements. Measurement levels manually identified by two observers were used as ground truth. The proposed solution obtained results comparable to the interobserver variability, presenting narrower limits of agreement for all measurements. Moreover, this solution proved to be fast, taking nearly 40 s (manual analysis took 3 min) to estimate the relevant measurements while being robust to the variation of the model's parameters. Overall, the proposed solution showed its potential for fast and robust estimation of the clinical measurements for occluding device selection, proving its added value for clinical practice.

Assessment of aortic valve tract dynamics using automatic tracking of 3D transesophageal echocardiographic images

The assessment of aortic valve (AV) morphology is paramount for planning transcatheter AV implantation (TAVI). Nowadays, pre-TAVI sizing is routinely performed at one cardiac phase only, usually at mid-systole. Nonetheless, the AV is a dynamic structure that undergoes changes in size and shape throughout the cardiac cycle, which may be relevant for prosthesis selection. Thus, the aim of this study was to present and evaluate a novel software tool enabling the automatic sizing of the AV dynamically in three-dimensional (3D) transesophageal echocardiography (TEE) images. Forty-two patients who underwent preoperative 3D-TEE images were retrospectively analyzed using the software. Dynamic measurements were automatically extracted at four levels, including the aortic annulus. These measures were used to assess the software's ability to accurately and reproducibly quantify the conformational changes of the aortic root and were validated against automated sizing measurements independently extracted at distinct time points. The software extracted physiological dynamic measurements in less than 2 min, that were shown to be accurate (error 2.2 ± 26.3 mm² and 0.0 ± 2.53 mm for annular area and perimeter, respectively) and highly reproducible (0.85 ± 6.18 and 0.65 ± 7.90 mm² of intra- and interobserver variability, respectively, in annular area). Using the maximum or minimum measured values rather than mid-systolic ones for device sizing resulted in a potential change of recommended size in 7% and 60% of the cases, respectively. The presented software tool allows a fast, automatic and reproducible dynamic assessment of the AV morphology from 3D-TEE images, with the extracted measures influencing the device selection depending on the cardiac moment used to perform its sizing. This novel tool may thus ease and potentially increase the observer's confidence during prosthesis' size selection at the preoperative TAVI planning.

Fast Segmentation of the Left Atrial Appendage in 3-D Transesophageal Echocardiographic Images

Left atrial appendage (LAA) has been generally described as "our most lethal attachment," being considered the major source of thromboembolism in patients with nonvalvular atrial fibrillation. Currently, LAA occlusion can be offered as a treatment for these patients, obstructing the LAA through a percutaneously delivered device. Nevertheless, correct device sizing is not straightforward, requiring manual analysis of peri-procedural images. This approach is suboptimal, time demanding, and highly variable between experts, which can result in lengthy procedures and excess manipulations. In this paper, a semiautomatic LAA segmentation technique for 3-D transesophageal echocardiography (TEE) images is presented. Specifically, the proposed technique relies on a novel segmentation pipeline where a curvilinear blind-ended model is optimized through a double stage strategy: 1) fast contour evolution using global terms and 2) contour refinement based on regional energies. To reduce its computational cost, and thus make it more attractive to real interventions, the B-spline explicit active surface framework was used. This novel method was evaluated in a clinical database of 20 patients. Manual analysis performed by two observers was used as ground truth. The 3-D segmentation results corroborated the accuracy, robustness to the variation of the parameters, and computationally attractiveness of the proposed method, taking approximately 14 s to segment the LAA with an average accuracy of ~0.9 mm. Moreover, a performance comparable to the interobserver variability was found. Finally, the advantages of the segmented model were evaluated, while semiautomatically extracting the clinical measurements for device selection, showing a similar accuracy but with a higher reproducibility when compared to the current practice. Overall, the proposed segmentation method shows potential for an improved planning of LAA occlusion, demonstrating its added value for normal clinical practice.

Artificial intelligence and echocardiography

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.