AI自动完成主动脉瓣置换术术前规划，2分钟内出结果。
TAVI-TEC: An AI-Based Tool for Procedural Planning of Transcatheter Aortic Valve Implantation
- 用深度学习自动分割血管、识别关键点，量化瓣环尺寸
- 测量结果与医生标注高度一致，瓣膜型号预测准确率达82%
- 适合心外科团队提速术前决策，减少人为差异
术前CTA对经导管主动脉瓣植入术（TAVI）至关重要，可提供瓣环和血管通路的解剖信息。随着手术量增加，提高效率与标准化标注日益重要。本研究提出TAVI-TEC，一个集成于Web DICOM查看器的全自动化AI框架，用于常规TAVI术前规划。基于使用SAPIEN 3 Ultra（S3U）瓣膜患者的术前CTA数据，采用全自动流程实现心血管结构分割、钙化检测、中心线提取、标志点识别及瓣环平面定义，量化关键瓣环与主动脉根部参数，并生成腔内狭窄与血管直径的颜色图以评估血管通路。通过多层感知机分类器预测术前瓣膜尺寸。结果显示，TAVI-TEC可在约2-6分钟内完成术前测量。瓣环面积（一致性系数CCC=0.934；组内相关系数ICC=0.935；R²=0.881）与周长（CCC=0.909；ICC=0.909；R²=0.854）与临床医生测量结果高度一致。瓣膜尺寸预测模型总体准确率为82%，多数误判发生在相邻尺寸间。尽管需进一步多中心验证并扩展至更多测量项和瓣膜平台，但TAVI-TEC有望降低术前测量的主观差异，优化心脏团队决策流程。
原文摘要 · Abstract (English)
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.
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