Leveraging CT-derived chronic imaging signatures for acute kidney injury evaluation.

Accurate estimation of baseline serum creatinine (SCr) remains an unmet clinical need in acute kidney injury (AKI) evaluation, as premorbid SCr values are often unavailable. This study aimed to develop and validate a baseline SCr prediction model using CT-derived imaging features to improve AKI diagnosis and staging. The study included patients with available baseline SCr who underwent abdominal CT at two tertiary hospitals. Kidney segmentation was performed using a fine-tuned Swin UNETR model, from which imaging features were extracted. We evaluated machine learning models incorporating imaging features for baseline SCr estimation and compared their estimates with those derived from the back-calculation method. Most selected imaging features were associated with the presence of preexisting chronic kidney disease. Across internal and external test sets, the tabular foundation models achieved the best performance (MAE, 0.154-0.158/0.168-0.174; RMSE, 0.253-0.261/0.220-0.231). AKI staging based on predicted baseline SCr showed substantially higher agreement with the ground truth than back-calculation (Cohen's κ, 0.69/0.60 vs. 0.31/0.36), while markedly reducing over-staging (8.7%/8.5% vs. 45.2%/37.6%). Our externally validated, image-driven prediction model enabled accurate baseline SCr estimation and improved AKI classification.