Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies

Table 2 Major artificial intelligence model categories for chronic kidney disease detection and early risk stratification

Model category	Data source	Typical task	Key performance range	Primary clinical use	Ref.
Clinical early-warning	EHR, claims, labs	6-12 months incident CKD prediction	AUROC 0.80-0.95	Trigger confirmatory testing/referral	[25,26]
Multimarker ML	Creatinine + cystatin C + labs	Threshold reclassification	↓ False negatives near cutoffs	Reduce misclassification	[16,29]
Imaging-based DL	CT, ultrasound	Structural injury detection	Dice 81%-94%; Acc 86%-90%	Noninvasive fibrosis/triage	[30,31]
Population screening	Registries, insurance	Risk stratification	AUROC 0.80-0.95	Targeted screening	[33]

Performance ranges reflect external validation when reported. CKD: Chronic kidney disease; CT: Computed tomography; AUROC: Area under the receiver operating characteristic curve; ML: Machine learning; DL: Deep learning.