Why the study?
CKD affects nearly 850 million individuals globally, with an undiagnosed prevalence of 60%, presenting an opportunity to leverage the relationship between CKD and cardiovascular disease for detection.
Does a deep learning model applied to parasternal long-axis echocardiography videos accurately detect chronic kidney disease?
Does a deep learning model applied to parasternal long-axis echocardiography videos accurately detect chronic kidney disease?
A deep learning model can noninvasively screen for chronic kidney disease using standard parasternal long-axis echocardiography videos with robust performance across multiple independent clinical sites.
May enable opportunistic CKD screening during routine echocardiography; extends AI applications to cardiorenal detection with multicenter validation.
Chronic kidney disease (CKD) affects nearly 850 million individuals globally; the prevalence of undiagnosed CKD is 60%. Taking advantage of the relationship between CKD and cardiovascular disease, we developed a deep learning (DL) model to detect CKD from parasternal long-axis (PLAX) videos using 325,377 PLAX videos from 62,818 patients at Cedars-Sinai Medical Center (CSMC). We externally validated our model in two independent cohorts of 2,224 patients at Stanford Healthcare (SHC) and 41,611 patients at Kaiser-Permanente Northern California (KPNC). In a held-out test cohort at CSMC, our model detected any stage of CKD with an area under the curve (AUC) of 0.756 [95% confidence interval 0.749 - 0.763], with consistently strong performance in KPNC (AUC 0.718 [0.714 - 0.723]) and SHC (AUC 0.719 [0.704 - 0.735]). Our DL echo model detected CKD with robust performance at two external clinical sites, offering an avenue for noninvasive screening and improved detection rates.
No takes yet. Share an insight, caveat, or question.
Yuan et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: