Inter- and intra-observer repeatability of aortic annulus measurements on screening CT for transcatheter aortic valve replacement (TAVR): Implications for appropriate device sizing

Practical approach to measuring vessels and reporting z-scores in children

Heart size and vessel diameters naturally increase with a child's growth, development, and needs. Measuring the size of blood vessels and tracking their growth have become a common practice among pediatric imaging specialists. Practitioners use tools like z-scores to standardize measurements against reference values that account for age, sex, and/or body size and habitus and help determine if vascular measurements deviate from what is expected in a healthy population. In this article, we review measurement techniques of significant vascular regions of interest in children covering "how to measure," "where to measure," and "sources of measurement errors." We also go over the concept of reporting z-scores in children with a review of the available literature and commonly used pediatric z-score calculators.

Automatic Aortic Valve Extraction Using Deep Learning with Contrast-Enhanced Cardiac CT Images

PURPOSE

This study evaluates the use of deep learning techniques to automatically extract and delineate the aortic valve annulus region from contrast-enhanced cardiac CT images. Two approaches, namely, segmentation and object detection, were compared to determine their accuracy.

MATERIALS AND METHODS

A dataset of 32 contrast-enhanced cardiac CT scans was analyzed. The segmentation approach utilized the DeepLabv3+ model, while the object detection approach employed YOLOv2. The dataset was augmented through rotation and scaling, and five-fold cross-validation was applied. The accuracy of both methods was evaluated using the Dice similarity coefficient (DSC), and their performance in estimating the aortic valve annulus area was compared.

RESULTS

The object detection approach achieved a mean DSC of 0.809, significantly outperforming the segmentation approach, which had a mean DSC of 0.711. Object detection also demonstrated higher precision and recall, with fewer false positives and negatives. The aortic valve annulus area estimation had a mean error of 2.55 mm.

CONCLUSIONS

Object detection showed superior performance in identifying the aortic valve annulus region, suggesting its potential for clinical application in cardiac imaging. The results highlight the promise of deep learning in improving the accuracy and efficiency of preoperative planning for cardiovascular interventions.

The Current Landscape of Artificial Intelligence in Imaging for Transcatheter Aortic Valve Replacement

Purpose

This review explores the current landscape of AI applications in imaging for TAVR, emphasizing the potential and limitations of these tools for (1) automating the image analysis and reporting process, (2) improving procedural planning, and (3) offering additional insight into post-TAVR outcomes. Finally, the direction of future research necessary to bridge these tools towards clinical integration is discussed.

Recent Findings

Transcatheter aortic valve replacement (TAVR) has become a pivotal treatment option for select patients with severe aortic stenosis, and its indication for use continues to broaden. Noninvasive imaging techniques such as CTA and MRA have become routine for patient selection, preprocedural planning, and predicting the risk of complications. As the current methods for pre-TAVR image analysis are labor-intensive and have significant inter-operator variability, experts are looking towards artificial intelligence (AI) as a potential solution.

Summary

AI has the potential to significantly enhance the planning, execution, and post-procedural follow up of TAVR. While AI tools are promising, the irreplaceable value of nuanced clinical judgment by skilled physician teams must not be overlooked. With continued research, collaboration, and careful implementation, AI can become an integral part in imaging for TAVR, ultimately improving patient care and outcomes.

Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis

The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI's promising role in the AS clinical pathway.

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.

Impact of observer experience on multi-detector computed tomography aortic valve morphology assessment and valve size selection for transcatheter aortic valve replacement

Transcatheter aortic valve replacement (TAVR) has become the standard treatment for aortic stenosis in older patients. It increasingly relies on accurate pre-procedural planning using multidetector computed tomography (MDCT). Since little is known about the required competence levels for MDCT analyses, we comprehensively assessed MDCT TAVR planning reproducibility and accuracy with regard to valve selection in various healthcare workers. 20 randomly selected MDCT of TAVR patients were analyzed using dedicated software by healthcare professionals with varying backgrounds and experience (two structural interventionalists, one imaging specialist, one cardiac surgeon, one general physician, and one medical student). Following the analysis, the most appropriate Edwards SAPIEN 3™ and Medtronic CoreValve valve size was selected. Intra- and inter-observer variability were assessed. The first structural interventionalist was considered as reference standard for inter-observer comparison. Excellent intra- and inter-observer variability was found for the entire group in regard to the MDCT measurements. The best intra-observer agreement and reproducibility were found for the structural interventionalist, while the medical student had the lowest reproducibility. The highest inter-observer agreement was between both structural interventionalists, followed by the imaging specialist. As to valve size selection, the structural interventionalist showed the highest intra-observer reproducibility, independent of the brand of valve used. Compared to the reference structural interventionalist, the second structural interventionalist showed the highest inter-observer agreement for valve size selection [ICC 0.984, 95% CI 0.969-0.991] followed by the cardiac surgeon [ICC 0.947, 95%CI 0.900-0.972]. The lowest inter-observer agreement was found for the medical student [ICC 0.507, 95%CI 0.067-0.739]. While current state-of-the-art MDCT analysis software provides excellent reproducibility for anatomical measurements, the highest levels of confidence in terms of valve size selection were achieved by the performing interventional physicians. This was most likely attributable to observer experience.

Evolving computed tomography angiography for aortic valve replacement: Optimizing transcatheter and surgical therapies

Transcatheter aortic valve replacement (TAVR) has transformed the treatment of aortic stenosis and pre-procedure planning relies heavily on advanced imaging. Multidetector computed tomography angiography, the "TAVR CT," facilitates essential planning steps of measuring the aortic root for valve sizing and feasibility and assessment of potential access vessels, making it the guideline gold standard in preprocedural TAVR work up. This Impact of Advanced Imaging Techniques on Cardiac Surgery article will examine the development of TAVR CT, illustrate the current impact and utility, and highlight potential areas of future growth. Clinicians who keep informed of these changes and can become proficient with TAVR CT analyses will offer patients the most optimal results and fuel future therapeutic growth.

Discrepancy of echocardiography and computed tomography in initial assessment and 2-year follow-up for monitoring Marfan syndrome and related disorders

Patients with Marfan syndrome and related disorders are at risk for aortic dissection and aortic rupture and therefore require appropriate monitoring. Computed tomography (CT) and transthoracic echocardiography (TTE) are routinely used for initial diagnosis and follow-up. The purpose of this study is to compare whole-heart CT and TTE aortic measurement for initial work-up, 2-year follow-up, and detection of progressive aortic enlargement. This retrospective study included 95 patients diagnosed with Marfan syndrome or a related disorder. All patients underwent initial work-up including aortic diameter measurement using both electrocardiography-triggered whole-heart CT and TTE. Forty-two of these patients did not undergo aortic repair after initial work-up and were monitored by follow-up imaging within 2 years. Differences between the two methods for measuring aortic diameters were compared using Bland-Altman plots. The acceptable clinical limit of agreement (acLOA) for initial work-up, follow-up, and progression within 2 years was predefined as <  ± 2 mm. Bland-Altman analysis revealed a small bias of 0.2 mm with wide limits of agreement (LOA) from + 6.3 to - 5.9 mm for the aortic sinus and a relevant bias of - 1.6 mm with wide LOA from + 5.6 to - 8.9 mm for the ascending aorta. Follow-up imaging yielded a small bias of 0.5 mm with a wide LOA from + 6.7 to - 5.8 mm for the aortic sinus and a relevant bias of 1.1 mm with wide LOA from + 8.1 to - 10.2 mm for the ascending aorta. Progressive aortic enlargement at follow-up was detected in 57% of patients using CT and 40% of patients using TTE. Measurement differences outside the acLOA were most frequently observed for the ascending aorta. Whole-heart CT and TTE measurements show good correlation, but the frequency of measurement differences outside the acLOA is high. TTE systematically overestimates aortic diameters. Therefore, whole-heart CT may be preferred for aortic monitoring of patients with Marfan syndrome and related disorders. TTE remains an indispensable imaging tool that provides additional information not available with CT.

Preoperative TAVR Planning: How to Do It

Transcatheter aortic valve replacement (TAVR) is a well-established treatment option for patients with severe symptomatic aortic stenosis (AS) whose procedural efficacy and safety have been continuously improving. Appropriate preprocedural planning, including aortic valve annulus measurements, transcatheter heart valve choice, and possible procedural complication anticipation is mandatory to a successful procedure. The gold standard for preoperative planning is still to perform a multi-detector computed angiotomography (MDCT), which provides all the information required. Nonetheless, 3D echocardiography and magnet resonance imaging (MRI) are great alternatives for some patients. In this article, we provide an updated comprehensive review, focusing on preoperative TAVR planning and the standard steps required to do it properly.

Fully automated measurement of aortic root anatomy using Philips HeartNavigator computed tomography software: fast, accurate, or both?

Cardiac computed tomography (CT) is vital for safety and efficacy of transcatheter aortic valve implantation (TAVI). We aimed to determine the accuracy of fully automated CT analysis of aortic root anatomy before TAVI by Philips HeartNavigator software. This prospective, academic, single-centre study enrolled 128 consecutive patients with native aortic valve stenosis considered for TAVI. Automated HeartNavigator software was compared to the standard manual CT analysis by experienced operators using FluoroCT software. The sizing of the aortic annulus by perimeter and area significantly differed between both methods: mean perimeter was 76.43 mm vs. 77.52 mm (P < 0.0001) using manual FluoroCT vs. automated HeartNavigator software; mean area was 465 mm2 vs. 476 mm2 (P < 0.0001). Interindividual variability testing revealed mean differences between the two operators were 1.21 mm for the aortic annulus perimeter and 9 mm2 for the aortic annulus area. The hypothetical self-expandable transcatheter prosthesis sizing resulted in 80% agreement in 80% of cases. The time required to perform the automated CT analysis was significantly shorter than the time required for manual analysis (mean 17.8 min vs. 2.1 min, P < 0.0001). Philips HeartNavigator fully automated software for pre-TAVI CT analysis is a promising technology. Differences detected in aortic annulus dimensions are small and similar to the variability of manual CT analysis. Automated prediction of optimal fluoroscopic viewing angles is accurate. Correct transcatheter prosthesis sizing requires clinical oversight.

Transcatheter Balloon-Expandable Valve-in-Valve to Treat Severe Paravalvular Leak Secondary to ACURATE-neo Self-expanding Prosthesis-Annulus Mismatch

A 75-year-old male with severe symptomatic aortic stenosis underwent transcatheter aortic valve implantation with a Large (27-mm) ACURATE-neo transcatheter aortic valve, complicated by severe paravalvular leak. He developed rapid and progressive worsening heart failure. Reanalysis of the computed tomography images suggested evidence of prosthesis-annulus mismatch. Therefore, a redo transcatheter aortic valve implantation utilizing a 29-mm SAPIEN 3 transcatheter aortic valve was performed. This case illustrates the importance of proper valve sizing to avoid paravalvular leak, and how to safely cross an ACURATE-neo valve to avoid catheter entangling.