CardioVision: Revolutionizing Patient-Specific Cardiovascular Treatment through AI-Enhanced Imaging and 3D Visualization Technologies

Introduction: CardioVision is an AI-tool designed to enhance the diagnosis and management of cardiovascular diseases, with aortic stenosis (AS) serving as a key example. Traditional imaging methods, such as echocardiography and computed tomography (CT), often lack the ability to provide a comprehensive real-time analysis of critical features like dimension measurements or calcium metrics, which are essential for assessing disease severity and predicting surgical outcomes. Conventional image processing techniques also struggle to accurately capture and quantify complex cardiac morphologies, limiting their utility in precise surgical planning and risk assessment. By harnessing advanced AI algorithms, CardioVision overcomes these challenges, delivering improved image analysis capabilities that enable more accurate diagnostics and effective treatment planning for cardiovascular conditions.

Materials and

Methods: The CardioVision pipeline begins with the acquisition of DICOM images of the heart. These images are first preprocessed to align and standardize the data, preparing it for the deep learning analysis. The system utilizes pretrained U-net models to accurately identify and predict critical left heart components such as the ascending aorta, root, coronaries, left ventricular outflow tract (LVOT), and left ventricle. Following the segmentation process, CardioVision detects anatomical landmarks and computationally constructs 3D models of the aortic valve, alongside measuring the dimensions of the aortic root—key parameters for surgical planning. Additionally, the system employs advanced thresholding techniques to detect and quantify calcifications in the valve and root, generating essential calcium metrics for patient-specific diagnosis. To facilitate the clinical application of these complex analyses, CardioVision also includes a front-end graphical user interface (GUI), which supports the real-time visualization of STL models derived from U-net predictions, enhancing the interactivity and usability of this AI-tool for medical professionals.

Results, Conclusions, and Discussions:

Results: CardioVision successfully processes raw CT data into detailed, patient-specific 3D models, adeptly capturing and quantifying complex features such as calcification patterns and critical cardiac anatomical landmarks. The system demonstrated high accuracy in modeling the aortic root and valve structures, which significantly enhances the interpretability of cardiac pathologies. With its advanced AI-driven pipeline, CardioVision has automatically processed over 200 patient cases, performing specific aortic analyses for each individual (Figure 1). These capabilities not only provide precise metrics essential for effective surgical planning but also contribute to a significant enhancement in both diagnostic and therapeutic strategies, optimizing patient outcomes in cardiovascular care.

Conclusions: CardioVision presents a significant advancement in cardiovascular diagnostics and treatment, particularly for aortic stenosis, by synthesizing AI-enhanced image analytics with sophisticated 3D visualization. This integration not only augments medical imaging but also enriches clinician capabilities in tailoring patient-specific therapies. Furthermore, the tool’s automated throughput of extensive patient datasets facilitates prospective studies into the correlation between aortic root morphology and clinical outcomes, potentially revolutionizing cardiovascular disease management and therapeutic precision.

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