Improving structural heart disease screening: AI-ECG and novice AI-guided focused cardiac ultrasound

Figures

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  Fig. 1. Study flow of the study participants. 995 patients were recruited within 30 days of having an AI-ECG. All underwent a focused cardiovascular ultrasound performed by one of 9 novice users guided by AI software (AI-FoCUS) and a diagnostic echocardiogram. Of the 995 AI-FoCUS scans, 97.3% were technically feasible; 2.7% (n = 27) were not and hence excluded. Created in BioRender. Kane, G. (2026).

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  Fig. 2. AI-FoCUS example. A screenshot of the UltraSight software, which depicts the AI-visual guide to users for probe position and image quality (a). The standard AI-FoCUS exam included the following six views: three from the parasternal acoustic window: b parasternal long axis view, c parasternal short axis at the aortic valve level, and d parasternal short axis at the mid-ventricular level, as well as three from the apical window with standard views of: e the apical 4 chamber, f 3 chamber, and g 2 chamber views.

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  Fig. 3. Relationship of user perceived difficulty with expert-rated image quality. Demonstrated in scatterplot format are the proportion of scans grouped by the perceived difficulty by the user (x-axis) against the image quality as graded by the expert (y-axis) for the parasternal long axis (a); parasternal short axis of the aortic valve level (b); and at the mid-ventricular level (c); and the apical 4-chamber (d); 3-chamber (e) and 2-chamber (f).

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  Fig. 4. Side-by-side confusion matrix plots. Demonstrated are the results for both the AI-ECG on the left and the AI-FoCUS on the right for the prediction of the composite structural heart disease endpoint against the ground truth of the echocardiogram. Note by study design all patients had an AI-ECG. The feasibility of the AI-focus was 97.3%; 2.7% (n = 27) were not feasible and hence excluded.

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  Fig. 5. Positive predictive values (PPVs) of the AI-ECG and AI-FoCUS screening pathways at various prevalences of structural heart disease. Shown are the measured PPVs in the overall cohort at 16.4% and in those without known structural heart disease (6.1%), and then simulated PPVs at prevalences of 5% and 2%.

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  Fig. 6. Models of the three testing strategies as screening tests and referrals that would be generated for echocardiography. The first using the AI-ECG with a high negative predictive value (NPV) but low positive predictive value (PPV) and was abnormal in 47.0% of cases. The second, where all patients undergo AI-FoCUS, has high PPV and NPV and has a lower abnormality rate/rate of referral for echo, and finally the two-step where only those patients with abnormal AI-ECG (48%) undergo an AI-FoCUS. This would generate the lowest referral for the echocardiogram and is less resource intensive than AI-FoCUS in all patients. Created in BioRender. Kane, G. (2026).

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  Fig. 7. Decision curve analysis for the composite structural heart disease endpoint. Standard decision curve analysis showing net benefit for referral to comprehensive echocardiography across threshold probabilities of 2% to 20% for AI-ECG alone, AI-FoCUS for all, and a two-step AI-ECG then AI-FoCUS strategy, together with the default strategies of referring all patients or referring none. Analyses were performed in the common interpretable cohort (n = 968), excluding 27 participants with uninterpretable AI-FoCUS studies. Among the three tested screening strategies, AI-FoCUS for all showed the highest net benefit across the plotted threshold range.

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  Fig. 8. Depicts current workflow (upper) where when patients develop symptoms suggestive of structural heart disease (SHD), they are referred for a medical evaluation with echocardiography often with a significant time delay due to availability. Integrating the results of the current work and others offer the potential for an alternative hypothetical future pathway (lower) where at risk, asymptomatic, populations get screened with an AI-ECG and those with positive results or patients with SHD symptoms undergo a more rapidly available novice AI-guided focused ultrasound (AI-FoCUS) scan which would triage diagnosis and priority to better determine those who need a medical expert evaluation and diagnostic testing and at what priority. Created in BioRender. Kane, G. (2026).