Deep learning-based echocardiography for cardiac assessment: a systematic literature review and meta-analysis

Figures

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  Figure 1. Flowchart for study selection following PRISMA guidelines. DL, deep learning; PRISMA, preferred reporting items for systematic reviews and meta-analyses. The flowchart depicts the flow of information throughout each step of the systematic review.

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  Figure 2. Timeline of published articles in the application of deep learning on echocardiography and their relation to advancements within the deep learning field. BERT, bidirectional encoder representations from transformers; ViT, vision transformer; CLIP, contrastive language-image pre-training; MAE, masked auto-encoder; SAM, segment anything. Classic CNN variants included 2D CNN, VGG, ResNet, DenseNet, EfficientNet, InceptionV3, MobileNetV2, XceptionNet, DCNN, 1D CNN, ConvNeXt, PWC-Net, and YoloV3, all of which are built on variations of convolutional layers for feature extraction in image or sequential data. Encoder-Decoders included U-Net, DeepLabV3, HRNet, Few-Shot Segmentation, and generic encoder modules, these networks are often used in segmentation and dense prediction. Spatiotemporal CNNs included 3D CNN, R2 + 1D, S3D, and SlowFast, all of which are designed to capture both spatial and temporal features for analysis of video or 3D data. Transformers included both ViT and ViViT (video vision transformer).

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  Figure 3. Proportion of the usage of individual cardiac views per study. The x-axis indicates the proportion of studies that used each specific chamber view. The number inside of each bar is the exact number of studies that used that view for each separate task. A) Prediction of disease or mortality. B) Replication of established echocardiographic measures. C) Segmentation of cardiac chambers. D) Classification of cardiac chamber views. E) Assessment of echocardiogram image quality.

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  Figure 4. Meta-analysis of the dice coefficient as a measure of performance for segmentation models stratified by cardiac chamber. DC, dice coefficient; CI, confidence interval. Proportions were calculated using the testing dataset. Sample size for all studies was provided in participants or transthoracic echocardiography studies. Studies reporting sample size in echocardiographic studies were included only when it could be reasonably interpreted from the text that a one-to-one correspondence with participants was intended. Such studies are marked with an *.

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  Figure 5. Meta-analysis of the overall accuracy for classification of cardiac chamber views. Acc, accuracy; CI, confidence interval. Proportion of correct classifications were calculated with the test dataset. Sample size for all studies was provided in participants.

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  Figure 6. Meta-analysis of the overall agreement of the fully automated video-based deep learning models with human measures of quality. Acc, accuracy; CI, confidence interval. Proportion of classification of quality in agreement with expert assessment was calculated using the test dataset. Sample size for all studies was provided in participants. Studies marked with an * are those were accuracy was estimated based on the provided individual accuracies per view.