The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

Figures

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  Figure 1. Temporal progression in automated quantification in echocardiography. Adapted from Nolan et al. (10). Permission obtained from the publisher.

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  Figure 2. Topological data analysis loop in aortic stenosis. The similarity network draws aggregation of patients with severe and mild AS in 2 opposite ends and moderate AS in the top and bottom arms of the loop. The red color represents the abnormal values, whereas blue represents the normal or minor malady in the disease space. (A) Spectrum of mean gradient in humans identified in the network of disease space. High gradients aggregated on the left side, whereas the low gradients were on the right side with moderate gradients in the middle. (B) Distribution of valve area on the network, (C) patient similarity network revealing preserved and reduced ejection fraction (EFs) in the patients with aortic stenosis (AS). The top arm clustered patients with the reduced EF, whereas the bottom arm described the patients with preserved EF. The red nodes consists of patients with EFs of ≤50%, whereas the blue nodes consists of patients with EFs of ≥65%. (D) Distribution of E/e' as a feature of diastolic dysfunction revealing the pattern of E/e' around moderate and severe AS. TDA, topological data analysis. Adapted from Casaclang-Verzosa et al. (51). Permission obtained from the publisher.

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  Figure 3. Functional significance of proximal left anterior descending artery stenosis. Adapted from Tang et al. (65). Permission obtained from the publisher.

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  Figure 4. Steps for building a machine learning pipeline and the reporting items in a checklist. Adapted from Sengupta et al. (2).