Yim Jeffrey, Mahdavi Mobina, Vaseli Hooman, Luong Christina, Tsang Michael Y C, Yeung Darwin F, Gin Ken, Barnes Marion E, Nair Parvathy, Jue John, Abolmaesumi Purang, Tsang Teresa S M
Division of Cardiology, University of British Columbia, 2775 Laurel Street, 9th Floor, Vancouver, BC, V5Z 1M9, Canada.
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Int J Cardiovasc Imaging. 2024 Oct;40(10):2157-2165. doi: 10.1007/s10554-024-03207-7. Epub 2024 Aug 10.
Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL) models. A total of 30,080 unique studies were included; 24,013 studies were used to train a convolutional neural network model to automatically assess, at end-diastole, LV internal diameter (LVID), interventricular septal wall thickness (IVS), posterior wall thickness (PWT), and LV mass. The model was trained to select end-diastolic frames with the largest LVID and to identify four landmarks, marking the dimensions of LVID, IVS, and PWT using manually labeled landmarks as reference. The model was validated with 3,014 echocardiographic cines and the accuracy of the model was evaluated with a test set of 3,053 echocardiographic cines. The model accurately measured LVID, IVS, PWT, and LV mass compared to study report values with a mean relative error of 5.40%, 11.73%, 12.76%, and 13.93%, respectively. The 𝑅 of the model for the LVID, IVS, PWT, and the LV mass was 0.88, 0.63, 0.50, and 0.87, respectively. The novel DL model developed in this study was accurate for LV dimension assessment without the need to select end-diastolic frames manually. DL automated measurements of IVS and PWT were less accurate with greater wall thickness. Validation studies in larger and more diverse populations are ongoing.
左心室（LV）几何模式有助于临床医生诊断和预测各种心肌病。本研究的目的是使用深度学习（DL）模型评估左心室尺寸和壁厚的准确性及可重复性。共纳入30080项独特的研究；其中24013项研究用于训练卷积神经网络模型，以在舒张末期自动评估左心室内径（LVID）、室间隔壁厚（IVS）、后壁厚度（PWT）和左心室质量。训练该模型以选择具有最大LVID的舒张末期帧，并使用手动标记的地标作为参考来识别四个地标，标记LVID、IVS和PWT的尺寸。该模型用3014份超声心动图电影进行验证，并用3053份超声心动图电影测试集评估模型的准确性。与研究报告值相比，该模型准确测量了LVID、IVS、PWT和左心室质量，平均相对误差分别为5.40%、11.73%、12.76%和13.93%。该模型对LVID、IVS、PWT和左心室质量的R值分别为0.88、0.63、0.50和0.87。本研究中开发的新型DL模型在无需手动选择舒张末期帧的情况下，对左心室尺寸评估准确。DL对IVS和PWT的自动测量在壁厚较大时准确性较低。正在更大和更多样化的人群中进行验证研究。
