Publication details
Enhancing TAVI Patient Evaluation: A User-Friendly Tool for CT-Derived Body Composition Assessment
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | Cor et Vasa |
| MU Faculty or unit | |
| Citation | |
| web | https://e-coretvasa.cz/artkey/cor-202503-0001_enhancing-tavi-patient-evaluation-a-user-friendly-tool-for-ct-derived-body-composition-assessment.php |
| Doi | https://doi.org/10.33678/cor.2025.008 |
| Keywords | Body composition; Obesity; Overall survival; Sarcopenia; Transcatheter aortic valve; implantation (TAVI) |
| Description | Background: CT-derived body composition analysis has emerged as a powerful prognostic tool for TAVI patient outcomes. However, widespread clinical implementation remains limited by complex software requirements and technical expertise barriers. This study aims to develop and validate an accessible web-based interface that streamlines the implementation of existing AutoMATiCA's validated CT-based body composition assessment in the pre-TAVI evaluation workflow. Methods: We developed a web-based interface integrating the validated AutoMATiCA's AI-driven segmentation software for automated body composition assessment. The system analyses pre-procedural CT scans to quantify Skeletal Muscle Index, Visceral Adipose Tissue, and Subcutaneous Adipose Tissue. The interface accepts DICOM files and patient data, generating comprehensive reports including segmented images and measurements. Results: System evaluation demonstrated an average analysis time of 21 seconds from upload to results display. User experience assessment with five clinicians showed unanimous positive feedback regarding accessibility and utility. Technical validation confirmed accurate tissue segmentation and quantification capabilities. Analysis of illustrative cases demonstrated significant discrepancies between BMI-based assessment and CT-derived body composition analysis, revealing conditions such as sarcopenic obesity and preserved muscle mass that would be missed by BMI evaluation alone. Conclusion: This technical solution provides an accessible, integrated approach to body composition assessment in TAVI patients. Building upon the validated AutoMATiCA software, the system successfully bridges the gap between complex analysis capabilities and clinical practicality through an intuitive user interface. This solution should enable more precise risk stratification and a more individualized approach to patients indicated for TAVI in the future. |