背景:据报道,整体纵向应变(GLS)比射血分数更具可重复性和预后性。自动化,透明的方法可以增加信任和吸收。
目的:作者开发了基于开放式机器学习的GLS方法,并使用UnityUK超声心动图AICollaborative的多专家共识对其进行验证。
方法:我们训练了一个多图像神经网络(Unity-GLS)来识别环,顶点,和心内膜曲线在6,819根尖4-,2-,和三室图像。外部验证数据集包括来自100个超声心动图的这3个视图。收缩末期和舒张末期框架分别由11位专家标记,以形成共识的描记和要点。他们还通过纵向功能的视觉分级来订购超声心动图。一位专家使用2个专有软件包计算了全球应变。
结果:中位数GLS,11位专家的平均值,为-16.1(IQR:-19.3至-12.5)。使用每个案例的专家共识测量作为参考标准,个别专家测量的绝对误差中位数为2.00GLS单位.相比之下,机器方法的错误是:Unity-GLS1.3,专有A2.5,专有B2.2。与专家共识值的相关性为个别专家0.85,Unity-GLS0.91,专有A0.73,专有B0.79。使用多专家视觉排名作为参考,个别专家应变测量发现,中位数等级相关性为0.72,Unity-GLS0.77,专有A0.70和专有B0.74。
结论:我们计算GLS的开源方法与专家的共识一样,与专家的个人测量和专有机器解决方案一样强烈。训练数据,代码,和训练有素的网络可以免费在线获得。
BACKGROUND: Global longitudinal strain (GLS) is reported to be more reproducible and prognostic than ejection fraction. Automated, transparent methods may increase trust and uptake.
OBJECTIVE: The authors developed open machine-learning-based GLS methodology and validate it using multiexpert consensus from the Unity UK Echocardiography AI Collaborative.
METHODS: We trained a multi-image neural network (Unity-GLS) to identify annulus, apex, and endocardial curve on 6,819 apical 4-, 2-, and 3-chamber images. The external validation dataset comprised those 3 views from 100 echocardiograms. End-systolic and -diastolic frames were each labelled by 11 experts to form consensus tracings and points. They also ordered the echocardiograms by visual grading of longitudinal function. One expert calculated global strain using 2 proprietary packages.
RESULTS: The median GLS, averaged across the 11 individual experts, was -16.1 (IQR: -19.3 to -12.5). Using each case\'s expert consensus measurement as the reference standard, individual expert measurements had a median absolute error of 2.00 GLS units. In comparison, the errors of the machine methods were: Unity-GLS 1.3, proprietary A 2.5, proprietary B 2.2. The correlations with the expert consensus values were for individual experts 0.85, Unity-GLS 0.91, proprietary A 0.73, proprietary B 0.79. Using the multiexpert visual ranking as the reference, individual expert strain measurements found a median rank correlation of 0.72, Unity-GLS 0.77, proprietary A 0.70, and proprietary B 0.74.
CONCLUSIONS: Our open-source approach to calculating GLS agrees with experts\' consensus as strongly as the individual expert measurements and proprietary machine solutions. The training data, code, and trained networks are freely available online.