Development of artificial intelligence-based slow-motion echocardiography and clinical usefulness for evaluating regional wall motion abnormalities.

The diagnostic accuracy of exercise stress echocardiography (ESE) for myocardial ischemia requires improvement, given that it currently depends on the physicians' experience and image quality. To address this issue, we aimed to develop artificial intelligence (AI)-based slow-motion echocardiography using inter-image interpolation. The clinical usefulness of this method was evaluated for detecting regional wall-motion abnormalities (RWMAs). In this study, an AI-based echocardiographic image-interpolation pipeline was developed using optical flow calculation and prediction for in-between images. The accuracy for detecting RWMAs and image readability among 25 patients with RWMA and 25 healthy volunteers was compared between four cardiologists using slow-motion and conventional ESE. Slow-motion echocardiography was successfully developed for arbitrary time-steps (e.g., 0.125×, and 0.5×) using 1,334 videos. The RWMA detection accuracy showed a numerical improvement, but it was not statistically significant (87.5% in slow-motion echocardiography vs. 81.0% in conventional ESE; odds ratio: 1.43 [95% CI: 0.78-2.62], p = 0.25). Interreader agreement analysis (Fleiss's Kappa) for detecting RWMAs among the four cardiologists were 0.66 (95%CI: 0.55-0.77) for slow-motion ESE and 0.53 (95%CI: 0.42-0.65) for conventional ESE. Additionally, subjective evaluations of image readability using a four-point scale showed a significant improvement for slow-motion echocardiography (2.11 ± 0.73 vs. 1.70 ± 0.78, p < 0.001).In conclusion, we successfully developed slow-motion echocardiography using in-between echocardiographic image interpolation. Although the accuracy for detecting RWMAs did not show a significant improvement with this method, we observed enhanced image readability and interreader agreement. This AI-based approach holds promise in supporting physicians' evaluations.

Sitting maneuver to uncover latent left ventricular outflow tract obstruction in patients without hypertrophic cardiomyopathy.

BACKGROUND: Left ventricular outflow tract obstruction [LVOTO; pressure gradient (PG) ≥30 mmHg] is observed in some patients without hypertrophic cardiomyopathy (HCM), and it may develop especially in older patients without HCM (non-HCM). The aim of this study is to investigate if the Valsalva or an upright sitting maneuver can unveil latent LVOTO in patients with non-HCM. METHODS: A total of 33 non-HCM patients with a late peaking or dagger-shaped pulsed Doppler waveform of the LVOT and PG <30 mmHg were included. The Doppler flow velocity of the LVOT was measured at rest, after the Valsalva and a sitting maneuver. Peak PG of ≥30 mmHg after either maneuver was defined as latent LVOTO. The angle between the left ventricular septum and the aorta in the parasternal long-axis view and the apical three-chamber view was measured. RESULTS: Twenty (61 %) of the 33 patients (mean age 74 ±â€¯9 years) were diagnosed with latent LVOTO. Of these, five (25 %) patients were diagnosed after both the Valsalva and sitting maneuver, and 15 (75 %) were diagnosed only after the sitting maneuver. The latent LVOTO group had a significantly smaller angle than the no-LVOTO group between the ventricular septum and the aorta in the parasternal long axis views (107 ±â€¯8° vs. 117 ±â€¯8°, p < 0.01). CONCLUSION: The sitting maneuver is better than the Valsalva maneuver in unveiling latent LVOTO in older, non-HCM patients.