Smarter FoCUS: AI-Guided Focused Cardiac Ultrasound Enables Novice Detection of Left Ventricular Dysfunction

Figures

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  Figure 1. Representative examples of novice-acquired parasternal short axis images in end-diastole (A) and end-systole (B) and by sonographers (C) and (D).

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  Figure 2. Quality and feasibility of image views. Bar graphs demonstrate the proportion of each view that was deemed in review as good or excellent quality, adequate diagnostic quality, poor quality or not obtained (missing). Views: Parasternal long axis (PSLA); Parasternal short axis at the mid ventricular level (PSSA); Apical 4 chamber view (Ap 4C); Apical 3 chamber view (Ap 3C); Apical 2 chamber view (Ap 2C).

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  Figure 3. For parasternal long axis images, a decline in the quality of the images was not associated with increasing age (A) but was associated with and increasing body mass index (B). For apical two chamber images, a decline in the quality of the images was not associated with increasing age (C) but was associated with and increasing body mass index (D).

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  Figure 4. Correlation of left ventricular (LV) ejection fractions (EF) estimates by blinded experts on focused images with quantitative LVEF by transthoracic echocardiography.

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  Figure 5. Side-by-side confusion matrix plots: demonstrated are the results for both the expert analysis on the left and the AI interpretation on the right for the prediction of a reduced left ventricular ejection fraction (EF < 40%).

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  Figure 6. A proposed two-step screening workflow for low ejection fraction (EF). All patients undergo an AI-assisted scan by novice operators. All studies interpreted by AI. Only those with either a low EF by AI, or uninterpretable by AI are read by a cardiologist (14.7% of total scans). Only 1/8th of all patients would be referred for confirmatory diagnostic echocardiography.

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  Figure 7. A confusion matrix plot demonstrating the results of the two-step model for the prediction of a reduced left ventricular ejection fraction (EF < 40%).