AI 中文总结
本研究提出基于AI的TAVI-TEC框架，可自动完成TAVI术前关键测量与假体尺寸预测，耗时短、测量一致性强，准确率达82%，或能减少操作者差异、简化术前流程。
AI 中文摘要
计算机断层血管造影（CTA）对于经导管主动脉瓣植入术（TAVI）术前规划至关重要，可提供假体尺寸选择和血管通路评估所需的解剖信息。随着TAVI手术量的增加，提高效率和标准化标注在临床实践中变得至关重要。本研究提出TAVI-TEC，这是一种完全自动化的基于人工智能的框架，集成到基于网络的DICOM查看器中，用于常规术前TAVI规划。对接受SAPIEN 3 Ultra（S3U）假体TAVI的患者术前CTA扫描采用完全自动化流程处理，实现心血管结构的深度学习分割、钙化检测、中心线提取、地标识别及瓣环平面定义，以量化关键瓣环和主动脉根部测量值，并生成血管通路所需的管腔狭窄和血管直径彩色编码图。训练多层感知器分类器，用于术前预测假体尺寸。结果显示，TAVI-TEC可在约2-6分钟内完成术前测量；瓣环面积与临床医生测量值具有强一致性（ concordance系数CCC=0.934，组内相关系数ICC=0.935，R²=0.881），瓣环周长也具有强一致性（CCC=0.909，ICC=0.909，R²=0.854）；瓣膜尺寸预测模型整体准确率达82%，多数误分类发生在相邻假体尺寸之间。尽管需要进一步多中心验证并扩展至更多测量指标和瓣膜平台，但TAVI-TEC方法可减少TAVI术前测量的操作者差异，简化心脏团队决策的术前工作流程。
英文摘要
Computed tomography angiography (CTA) is crucial for preprocedural TAVI planning, providing the anatomical information required for prosthesis sizing and vascular access assessment. As the volume of TAVI procedure increases, improving efficiency and standardizing annotations is becoming essential in clinical practice. This study presents TAVI-TEC, a fully automated artificial intelligence-based framework integrated into a web based DICOM viewer for routine preoperative TAVI planning. Pre-procedural CTA scans from patients undergoing TAVI with SAPIEN 3 Ultra (S3U) prostheses were processed using a fully automated pipeline. Deep learning-based segmentation of cardiovascular structures, calcification detection, centerline extraction, landmark identification, and annular plane definition was implemented to quantify key annular and aortic root measurements and color-coded maps of lumen reduction and vessel diameter for vascular access. A multilayer perceptron classifier was trained to predict prosthesis size prior to the TAVI procedure. Results revealed that TAVI-TEC enabled pre-procedural measurements in approximately 2-6 min. Strong agreement with clinician-derived measurements was observed for annular area (coefficient of concordance, CCC = 0.934; interclass correlation coefficient, ICC = 0.935; R^2 = 0.881) and perimeter (CCC = 0.909; ICC = 0.909; R^2 = 0.854). The valve-size prediction model achieved 82% overall accuracy, with most misclassifications occurring between adjacent prosthesis sizes. Though further multicenter validation and extension to additional measurements and valve platforms are required, the TAVI-TEC methodology may reduce operator variability in pre-TAVI measurements and streamline the preoperative workflows of the Heart Team for decision-making.
