Deep learning-enabled screening of chronic kidney disease from echocardiography

Chronic kidney disease (CKD) affects nearly 850 million individuals globally. The prevalence of undiagnosed CKD is over 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 2,762 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 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.

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.

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).