Additional file 1 of Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography
Creators: name: Zha, Sigurd Zijun
name: Rogstadkjernet, Magnus
name: Klæboe, Lars Gunnar
name: Skulstad, Helge
name: Singstad, Bjørn-Jostein
name: Gilbert, Andrew
name: Edvardsen, Thor
name: Samset, Eigil
name: Brekke, Pål Haugar
Publisher: name: figshare
Year: 2023
Subjects: subject: Space Science
subject: Medicine
subject: Physiology
subject: FOS: Biological sciences
subject: Pharmacology
subject: Biotechnology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Marine Biology

description: Additional file 1: Supplemental Table 1. Distribution of patients and echocardiographic views in the training and test set. Supplemental Table 2. Distribution of repeated LVOTd measurements for patients in the training and test set. Supplemental Matrix 1. Data quality labels for the training set. Supplemental Matrix 2. Data quality labels for the test set. Supplemental Table 3. Means and medians from 5-fold validation of common data extension methods and alternate loss functions. Best values are marked in bold. Supplemental Table 4. Means and medians from 5-fold validation of various data configurations for model training. Best values are marked in bold. Supplemental Figure 1. Bland-Altman plot comparing the DL predicted and clinical reference LVOTds in pixel values. Limits of agreement were -6.43 to 6.30 pixels. The grey dotted line denotes the signed mean. The red dotted lines denote the limits of agreement. High, Medium and Low denote the image quality of the echocardiographic still frame. Supplemental Figure 2. Correlation plot comparing the DL predicted and clinical reference LVOTds in pixel values. Pearson R was 0.97 (p &lt; 0.001). The grey line denotes the reference. The red line denotes best fit. High, Medium and Low denote the image quality of the echocardiographic still frame. Supplemental Table 5. Absolute- and relative LVOTd errors for the DL model on the test set derived from pixel values. Supplemental Table 6. Mean point-wise ED in pixels and angle deviation for the DL model on the test set derived from pixel values. Supplemental Figure 3. Box-plots comparing precision for the DL model and clinicians for patients with exactly 3 repeated LVOTd measurements in the test set. Solid boxes represent the interquartile range. The whiskers represent the upper and lower adjacent values. The dots represent the outliers. Supplemental Table 7. Precision with coefficient of variation for the DL model and clinicians for patients with exactly 3 repeated LVOTd measurements in the test set. Supplemental Figure 4. Box-plots showing the individual effect of image quality and ground truth quality on LVOTd error for the DL predictions on the test set. Solid boxes represent the interquartile range. The whiskers represent the upper and lower adjacent values. Supplemental Table 8. Absolute- and relative LVOTd errors for the DL model on images the test set in grouped by individual image and ground truth quality labels. Supplemental Figure 5. Line-plot showing the effect of patient number and use of data extension methods during DL model training on mean absolute LVOTd error for DL predictions on the test set. Supplemental Table 9. Absolute LVOTd error and mean point-wise ED for a DL implementation using a standard U-Net. Only image augmentations were used as data extension methods since a standard U-Net does not have pre-trained ImageNet weights. Supplemental Table 10. Absolute LVOTd error and mean point-wise ED for a DL implementation using a U-Net with a ResNet50 encoder. Both image augmentations and pre-trained ImageNet weights were used as data extension methods.