Real-time deep learning-based image guiding and automated left ventricular measurements to reduce test-retest variability.

AIMS: To evaluate the effect of combining real-time deep learning (DL)-based guiding and automated measurements of left ventricular (LV) volumetric measurements and strain. METHODS AND RESULTS: Patients (n=47) with mixed cardiac pathology were examined by two sonographers and one reference cardiologist. A real-time DL guiding tool to avoid LV foreshortening was used by one sonographer only per patient. Automated DL-based measurements from the sonographer using the guiding tool were paired with automated measurements from the reference cardiologist (artificial intelligence (AI)-assisted echocardiography), while manual measurements from the sonographer not using the guiding tool were paired with manual measurements from the reference cardiologist (standard echocardiography). The variability of LV EDV, LV ESV, ejection fraction (LV EF) and global longitudinal strain (LV GLS) was compared for standard echocardiography versus AI-assisted echocardiography. Coefficients of variation were lower for AI-assisted echocardiography compared with standard echocardiography (6% vs 15% for LV EDV (p<0.001), 10% vs 19% for ESV (p<0.001) and 7% vs 11% for GLS (p=0.047), respectively). For LV EF, the coefficients of variation were similar across groups (8% vs 9%, p=0.503, respectively). In exploratory analyses, automated measurements alone (all p≤0.002) but not the guiding tool (all ≥0.199) explained the improved variability for LV EDV, ESV and GLS. CONCLUSIONS: AI-assisted echocardiography combining DL-based real-time guiding and automated measurements significantly reduced the variability of LV EDV, ESV and GLS when compared to standard echocardiography. Among experienced operators, automated measurements were more beneficial than real-time guiding. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, ID: NCT04580095.