Development and validation of a deep learning-based fully automated algorithm for pre-TAVR CT assessment of the aortic valvular complex and detection of anatomical risk factors: a retrospective, multicentre study

Aortic shape and diameter variations are predictive of short-term complications in transcatheter aortic valve replacement

INTRODUCTION

Anatomic and geometric considerations are critical components for transcatheter aortic valve replacement (TAVR) procedural planning. Aortic root geometry and 3-dimensional orientation have been previously associated with short-term complications but with mixed and inconsistent results. The purpose of this study was to investigate aortic 3-dimensional anatomical shape variants identified by principal component analysis (PCA) and whether these variants are associated with short-term complications.

METHODS

Pre-TAVR planning chest CT angiograms (N = 100) were analyzed to create 3-dimensional anatomic aortic models were subjected to PCA. Aortic shape variants described by principal components (PCs) and their respective scores were calculated for each patient in addition to standard planning geometric parameters. A short-term composite complication outcome within 1-month from the implantation included major and minor stroke, life-threatening and major bleeding, stage 3 acute kidney injury, new heart block and moderate plus paravalvular leak (PVL).

RESULTS

A total of 25 patients (25%) experienced perioperative complications following TAVR. Shape based PCs were: PC1 - variation in aortic arch height, isthmic angle, and aortic arch angle; PC2 aortic length; PC3- aortic tilt. Diameter based PCs described: PC1- diameter size along the entire aortic length; PC2- aortic diameter tapering, PC3- ascending to arch diameter ratio. On univariable logistic regression, four variables were predictive of periprocedural complications, including the ascending aortic diameter at the level of Valsalva sinuses (OR: 0.88 (95%CI: 0.78-1.00), P = 0.044), PC1-shape scores (OR: 1.01 (95%CI: 1.00-1.02), P = 0.011), PC2-shape scores (OR: 0.98 (95%CI: 0.97-1.00), P = 0.034), and PC-1 diameter scores (OR: 0.98 (95%CI: 0.96-1.00), P = 0.023). An optimized multivariable model considering only PC1-shape and PC1-diameter revealed a C-statistic of 0.76 with a sensitivity of 92.0% and specificity of 32.0%.

CONCLUSION

Aortic shape variants combining increased aortic arch height, acute isthmic angle, and mild aortic arch angle as identified by PCA were associated along with aortic size with higher rates of periprocedural complications in patients undergoing transfemoral TAVR. PCA identified shape variations outperformed standard 2-dimensional geometric measurements and could be considered as part of risk stratification prior to TAVR planning.

Clinical value of aortic arch morphology in transfemoral TAVR: artificial intelligence evaluation

BACKGROUND

The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study was to evaluate the AA morphology of patients who had TF-TAVR using an artificial intelligence algorithm and then to evaluate its predictive value for clinical outcomes.

MATERIALS AND METHODS

A total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study. The AA measurements were evaluated by deep learning, and then the approach index (I A ) was determined. The machine learning algorithm was used to construct the predictive model and was validated externally.

RESULTS

The area under the curve of the I A model using random forest and logistic regression was 0.675 [95% confidence interval (CI): 0.586-0.764] and 0.757 (95% CI: 0.665-0.849), respectively. The I A model was validated externally, and consistent distinctions were obtained. After we used a generalized propensity score matching method for continuous exposure, the I A was the strongest correlation factor for major procedural events (odds ratio: 3.87; 95% CI: 2.13-7.59, P < 0.001). When leaflet morphology or transcatheter heart valve type was an interactive item with I A , neither of them was statistically significant in terms of clinical outcomes.

CONCLUSION

I A may be used to identify the impact of AA morphology on procedural and clinical outcomes in patients having TF-TAVR and to help to predict the procedural complications.

Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care

Artificial intelligence (AI) serves as a powerful tool that can revolutionize how personalized, patient-focused care is provided within interventional cardiology. Specifically, AI can augment clinical care across the spectrum for acute coronary syndrome, coronary artery disease, and valvular heart disease, with applications in coronary and structural heart interventions. This has been enabled by the potential of AI to harness various types of health data. We review how AI-driven technologies can advance diagnosis, preprocedural planning, intraprocedural guidance, and prognostication in interventional cardiology. AI automates clinical tasks, increases efficiency, improves reliability and accuracy, and individualizes clinical care, establishing its potential to transform care. Furthermore, AI-enabled, community-based screening programs are yet to be implemented to leverage the full potential of AI to improve patient outcomes. However, to transform clinical practice, AI tools require robust and transparent development processes, consistent performance across various settings and populations, positive impact on clinical and care quality outcomes, and seamless integration into clinical workflows. Once these are established, AI can reshape interventional cardiology, improving precision, efficiency, and patient outcomes.

The role and mechanism of protein post‑translational modification in vascular calcification (Review)

Vascular calcification is closely associated with morbidity and mortality in patients with chronic kidney disease, atherosclerosis and diabetes. In the past few decades, vascular calcification has been studied extensively and the findings have shown that the mechanism of vascular calcification is not merely a consequence of a high-phosphorus and high-calcium environment but also an active process characterized by abnormal calcium phosphate deposition on blood vessel walls that involves various molecular mechanisms. Recent advances in bioinformatics approaches have led to increasing recognition that aberrant post-translational modifications (PTMs) play important roles in vascular calcification. This review presents the latest progress in clarifying the roles of PTMs, such as ubiquitination, acetylation, carbamylation and glycosylation, as well as signaling pathways, such as the Wnt/β-catenin pathway, in vascular calcification.

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.

Evolving capabilities of computed tomography imaging for transcatheter valvular heart interventions - new opportunities for precision medicine

The last decade has witnessed a substantial growth in percutaneous treatment options for heart valve disease. The development in these innovative therapies has been mirrored by advances in multi-detector computed tomography (MDCT). MDCT plays a central role in obtaining detailed pre-procedural anatomical information, helping to inform clinical decisions surrounding procedural planning, improve clinical outcomes and prevent potential complications. Improvements in MDCT image acquisition and processing techniques have led to increased application of advanced analytics in routine clinical care. Workflow implementation of patient-specific computational modeling, fluid dynamics, 3D printing, extended reality, extracellular volume mapping and artificial intelligence are shaping the landscape for delivering patient-specific care. This review will provide an insight of key innovations in the field of MDCT for planning transcatheter heart valve interventions.

A fully automated artificial intelligence-driven software for planning of transcatheter aortic valve replacement

BACKGROUND

Transcatheter aortic valve replacement (TAVR) is increasingly performed for the treatment of aortic stenosis. Computed tomography (CT) analysis is essential for pre-procedural planning. Currently available software packages for TAVR planning require substantial human interaction. We describe development and validation of an artificial intelligence (AI) powered software to automatically rend anatomical measurements and other information required for TAVR planning and implantation.

METHODS

Automated measurements from 100 CTs were compared to measurements from three expert clinicians and TAVR operators using commercially available software packages. Correlation coefficients and mean differences were calculated to assess precision and accuracy.

RESULTS

AI-generated annular measurements had excellent agreements with manual measurements by expert operators yielding correlation coefficients of 0.97 for both perimeter and area. There was no relevant bias with a mean difference of -0.07 mm and - 1.4 mm2 for perimeter and area, respectively. For the ascending aorta measured 5 cm above the annular plane, correlation coefficient was 0.95 and mean difference was 1.4 mm. Instruction for use-based sizing yielded agreement with the effective implant size in 87-88 % of patients for self-expanding valves (perimeter-based sizing) and in 88 % for balloon-expandable valves (area-based sizing).

CONCLUSIONS

A fully automated software enables accurate and precise anatomical segmentation and measurements required for TAVR planning without human interaction and with high reliability.

How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives

The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.