Abstract 14515: Initial Experience With Artificial Intelligence-Guided Echocardiography Acquisition in Rural America: Implementation and Quality Assessment

Abstract

Background: Use of artificial intelligence-guided echocardiography (AI echo) may increase access to imaging, but there is little experience of its application in rural and low resource settings. The Risk Underlying Rural Areas Longitudinal (RURAL) cohort is the first NHLBI population-based cohort study to employ AI echo. Hypothesis: AI echo is feasible and produces adequate quality images to assess cardiac structure and function among rural populations. Methods: The RURAL study, in partnership with Caption Health (Brisbane, CA), is using AI echo in a multiethnic cohort of 4600 participants, performed in a mobile exam unit (MEU) in 10 rural U.S. communities. Non-sonographer MEU technicians underwent 10 hrs in-person competency-based training before scanning. Cardiac structure and function were analyzed in an independent core laboratory. Ejection fraction (EF) was visually estimated and calculated using Caption Health’s Auto EF technology. Results: Overall, 138 participants had AI echoes analyzed from Sept 2021 to May 2022 of whom 62% were women, with 70% obese (body mass index (BMI) ≥ 30kg/m 2 ), median BMI 33.8kg/m 2 (Table 1). Median time per scan was 20.0mins (15.5, 29.7). Image quality was adequate for visual EF in 97%, with left ventricular dimensions measurable in 88% and left atrial diameter in 91%. Adequate images of the right heart and ascending aorta were less common: base of the right ventricle measurable in 62%, right atrium 61% and ascending aorta 60%. Most participants (96%) had LVEF ≥ 50% by visual estimation, and there was 96% agreement between visual and Auto EF for this group. Image quality, but not measurability, varied with BMI. Conclusion: AI echo imaging technology can be used by non-sonographers to acquire adequate quality images characterizing cardiac structure and function in a rural and predominantly obese population, suggesting utility across the spectrum of BMI and applicability in low resource environments with limited access to healthcare.

Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study

“…In a preliminary analysis of 138 AI-analyzed echocardiograms, there was adequate image quality for visual EF assessment in 97%, with LV dimensions being measurable in 88% and left atrial diameter in 91%. 46 For most participants (96%), the auto-EF corresponded with visual estimation. These results highlight the promising utility of AI Echo in limited health care and low-resource environments with high degrees of obesity (70% with body mass index ≥30 kg/m 2 ).…”