Deep learning-enabled screening of chronic kidney disease from echocardiography.

BACKGROUND: Chronic kidney disease (CKD) affects nearly 850 million individuals globally. The prevalence of undiagnosed CKD is over 60%. METHODS: 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 2,762 patients at Stanford Healthcare (SHC) and 41,611 patients at Kaiser-Permanente Northern California (KPNC). FINDINGS: 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 the KPNC (AUC: 0.718 [0.714-0.723]) and SHC (AUC: 0.719 [0.704-0.735]) cohorts. Our model performed well across subgroups with and without metabolic comorbidities, suggesting that it learned imaging features specific to CKD. CONCLUSIONS: Our DL echo model detected CKD with robust performance at two external clinical sites, thus offering an avenue for noninvasive screening and improved detection rates. FUNDING: This work was supported by the Sarnoff Cardiovascular Research Foundation, the American Heart Association (25POST1357984 and 25AHAI1487693), and the National Institutes of Health (R00HL157421, R01HL173487, and R01HL173526).