Scuoppo Roberta, Castelbuono Salvatore, Cannata Stefano, Gentile Giovanni, Agnese Valentina, Bellavia Diego, Gandolfo Caterina, Pasta Salvatore
Department of Engineering, Università degli Studi di Palermo, Viale Delle Scienze Ed.8, Palermo, Italy.
Department of Research, IRCCS ISMETT, via Tricomi, 5, Palermo, Italy.
Med Biol Eng Comput. 2025 Feb;63(2):467-482. doi: 10.1007/s11517-024-03215-8. Epub 2024 Oct 10.
In silico trials using computational modeling and simulations can complement clinical trials to improve the time-to-market of complex cardiovascular devices in humans. This study aims to investigate the significance of synthetic data in developing in silico trials for assessing the safety and efficacy of cardiovascular devices, focusing on bioprostheses designed for transcatheter aortic valve implantation (TAVI).
A statistical shape model (SSM) was employed to extract uncorrelated shape features from TAVI patients, enabling the augmentation of the original patient population into a clinically validated synthetic cohort. Machine learning techniques were utilized not only for risk stratification and classification but also for predicting the physiological variability within the original patient population.
By randomly varying the statistical shape modes within a range of ± 2σ, a hundred virtual patients were generated, forming the synthetic cohort. Validation against the original patient population was conducted using morphological measurements. Support vector machine regression, based on selected shape modes (principal component scores), effectively predicted the peak pressure gradient across the stenosis (R-squared of 0.551 and RMSE of 11.67 mmHg). Multilayer perceptron neural network accurately predicted the optimal device size for implantation with high sensitivity and specificity (AUC = 0.98).
The study highlights the potential of integrating computational predictions, advanced machine learning techniques, and synthetic data generation to improve predictive accuracy and assess TAVI-related outcomes through in silico trials.
使用计算建模和模拟进行的计算机模拟试验可以补充临床试验，以缩短复杂心血管设备在人体中的上市时间。本研究旨在探讨合成数据在开展计算机模拟试验以评估心血管设备安全性和有效性方面的重要性，重点关注用于经导管主动脉瓣植入术（TAVI）的生物假体。
采用统计形状模型（SSM）从TAVI患者中提取不相关的形状特征，从而将原始患者群体扩充为经过临床验证的合成队列。机器学习技术不仅用于风险分层和分类，还用于预测原始患者群体中的生理变异性。
通过在±2σ范围内随机改变统计形状模式，生成了100名虚拟患者，形成了合成队列。使用形态学测量对原始患者群体进行验证。基于选定形状模式（主成分得分）的支持向量机回归有效地预测了狭窄处的峰值压力梯度（决定系数为0.551，均方根误差为11.67mmHg）。多层感知器神经网络以高灵敏度和特异性准确预测了植入的最佳设备尺寸（曲线下面积=0.98）。
该研究强调了整合计算预测、先进机器学习技术和合成数据生成以提高预测准确性并通过计算机模拟试验评估TAVI相关结果的潜力。
