利用生成式对抗网络,在手术前预测方坦手术后的时变边界条件的主动机器学习。

IF 3 2区 医学 Q3 ENGINEERING, BIOMEDICAL
Wenyuan Song, David Frakes, Lakshmi Prasad Dasi
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引用次数: 0

摘要

丰坦(Fontan)手术是治疗先天性单心室小儿患者的最终方法。丰坦手术的手术规划是优化手术效果的有效手段,术前测量结果可作为术后模拟的边界条件。然而,实际术后测量结果可能与术前状态大相径庭,这就对手术规划的准确性提出了质疑。本研究的目的是应用机器精益技术来描述丰坦患者术前和术后的腔静脉血流状况,从而对手术规划中使用的术后边界条件进行预测。基于由整块参数模型合成的虚拟队列,我们提出了一种新颖的多样性感知生成式对抗主动学习框架,在心血管研究通常面临的非常有限的病例上成功地训练了预测性深度神经网络。14 组实验将不同的数据查询策略、指标和数据增强选项与生成式对抗网络独特地结合在一起,结果表明所提出的方法具有最高的整体预测准确性和决定系数。该框架是深度学习用于心血管血流预测/回归的第一步,降低了标记要求,扩大了学习空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks.

The Fontan procedure is the definitive palliation for pediatric patients born with single ventricles. Surgical planning for the Fontan procedure has emerged as a promising vehicle toward optimizing outcomes, where pre-operative measurements are used prospectively as post-operative boundary conditions for simulation. Nevertheless, actual post-operative measurements can be very different from pre-operative states, which raises questions for the accuracy of surgical planning. The goal of this study is to apply machine leaning techniques to describing pre-operative and post-operative vena caval flow conditions in Fontan patients in order to develop predictions of post-operative boundary conditions to be used in surgical planning. Based on a virtual cohort synthesized by lumped-parameter models, we proposed a novel diversity-aware generative adversarial active learning framework to successfully train predictive deep neural networks on very limited amount of cases that are generally faced by cardiovascular studies. Results of 14 groups of experiments uniquely combining different data query strategies, metrics, and data augmentation options with generative adversarial networks demonstrated that the highest overall prediction accuracy and coefficient of determination were exhibited by the proposed method. This framework serves as a first step toward deep learning for cardiovascular flow prediction/regression with reduced labeling requirements and augmented learning space.

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来源期刊
Annals of Biomedical Engineering
Annals of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
7.50
自引率
15.80%
发文量
212
审稿时长
3 months
期刊介绍: Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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