Virtual Reality and AI: Better Planning for Mitral Valve Repair and Grafts

Technological mutation in cardiology: the contribution of VR and AI

The landscape of interventional cardiology and cardiac surgery is undergoing an unprecedented technological transformation. The integration of virtual reality (VR) and artificial intelligence (AI) is no longer a futuristic perspective, but a clinical reality redefining procedural planning, the execution of complex procedures, and medical training. While these tools promise to refine surgical precision and personalize care, their application remains heterogeneous depending on the pathologies and specific clinical needs.

This literature review aimed to precisely map the anatomical fields of application for VR and AI within cardiac interventions. The objective was to analyze the scopes of use for these technologies as well as the radiological imaging modalities involved in image reconstruction for virtual reality. By synthesizing data from three systematic reviews encompassing 71 studies, this work evaluates how AI and VR coordinate to transform current standards, from decision support in heart transplantation to surgical simulation for valve repairs or conotruncal anomalies.

Methodology of the synthesis

This study is a literature review specifically targeting other systematic reviews to analyze the integration of virtual reality (VR) and artificial intelligence (AI) in cardiac interventions. Unlike a direct experimental study, the authors synthesized pre-existing data to evaluate the anatomical fields of application and the imaging modalities involved.

• Search strategy: The search was conducted on PubMed, Scopus, and Google Scholar databases.
• Keywords used: "reviews", "artificial intelligence", "virtual reality", and "cardiac interventions".
• Inclusion criteria: Only systematic reviews published in English were included.
• Exclusion criteria: Narrative reviews, original research articles (individual), editorials, and position papers were systematically excluded to ensure the rigor of the processed data.

The final corpus selected for analysis includes 3 systematic reviews, encompassing a total of 71 individual studies. The analysis focused on image reconstruction (via CT angiography and cardiac MRI), procedural planning for conotruncal anomalies, mitral valve repair, and AI-driven decision support in the context of heart transplantation.

Analysis of VR and AI applications in interventional cardiology

This literature review synthesized data from 3 systematic reviews, encompassing a total of 71 individual studies. The analysis highlights a marked specialization of technological tools according to clinical needs: training, planning, or decision support.

The results highlight two major areas for virtual reality:

• Training: VR is primarily used to refine technical skills in mitral valve repair, allowing for practice in a simulated environment before the actual procedure.
• Planning: For complex cases, particularly conotruncal anomalies, VR stands out as the tool of choice for preoperative spatial visualization.

Regarding artificial intelligence, its application is predominant in the field of heart transplantation. The authors report that AI excels in analyzing massive data for pattern detection, risk assessment, and clinical outcome prediction.

Qualitatively, the study highlights that image reconstruction for VR relies almost exclusively on cardiac magnetic resonance imaging (CMR) and computed tomography (CT). Although effective, these modalities are identified as bottlenecks due to their high cost and the time required for data processing.

Clinical analysis and limits of technological integration

The data from this review, synthesizing 71 studies across three systematic reviews, reveal a clear segmentation of technological applications in cardiology. Virtual reality (VR) is establishing itself as a standard for managing complex structures: it is preferred for mitral valve repair training and for preoperative planning of conotruncal anomalies. Conversely, artificial intelligence (AI) finds its maximum utility in managing decisional data, particularly for decision support in heart transplantation, where predictive analysis surpasses human synthesis capabilities.

However, the authors highlight major barriers to systemic implementation. The dependence of VR on computed tomography (CT) and cardiac MRI (CMR) for image reconstruction creates a bottleneck. These imaging modalities, although precise, are identified as costly and time-consuming, which limits access to these simulation tools in real-time or for less critical cases.

Clinically, this means that while VR and AI improve surgical safety and training, their economic efficiency remains to be optimized. For the cardiac surgeon, these results confirm that investment in VR is currently more profitable for complex structural pathologies (conotruncal), while AI should be perceived as a tool for securing transplantation pathways. Generalization will require a simplification of image acquisition protocols to feed these virtual models.

Summary of results

This analysis of three systematic reviews including 71 studies confirms the technological specialization by field: virtual reality (VR) excels in training for mitral surgery and planning for conotruncal anomalies, while artificial intelligence (AI) dominates decision support in heart transplantation. The study notes that the effectiveness of VR remains, however, dependent on high-resolution imaging protocols (MRI, CT scan), which are costly and time-consuming.

In concrete terms, for the practitioner:

• Securing procedures: Use VR to simulate mitral valve repairs, a field where the pedagogical contribution is best documented by this synthesis.
• Surgical planning: Adopt immersive reconstruction for the management of conotruncal congenital heart diseases to improve preoperative spatial accuracy.
• Decision support: Integrate AI algorithms to refine risk assessment and outcome prediction, particularly in heart transplantation.
• Logistical anticipation: Plan for increased lead times in the workflow for acquiring CMR or CT images required for the implementation of high-performance VR tools.

Technical lexicon of the study

CMR (Cardiac Magnetic Resonance): Cardiac magnetic resonance imaging technique used for high-fidelity image reconstruction in virtual reality, although noted as costly and time-consuming in this review.

CT Angiography (Angioscanner): Radiological vessel imaging modality using a scanner, identified as one of the primary data sources for pre-interventional anatomical modelling in VR.

Conotruncal anomalies: Congenital heart defects (affecting the outflow tracts) representing, according to the 71 studies analyzed, the most frequent clinical application for surgical planning using virtual reality.

Mitral valve repair: Structural cardiac surgery procedure constituting the predominant field of application for surgical training via virtual reality technologies.

Heart transplantation: Specific field of cardiology where decision support systems based on artificial intelligence (AI) are the most widely deployed for pattern analysis and personalized care.

Image reconstruction: Process of converting radiological data (DICOM) into immersive three-dimensional models allowing practitioners to visualize pathological anatomy in a virtual environment.

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