A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images

Abstract

Chronic kidney disease (CKD) is a global health challenge, but invasive renal biopsies, the gold standard for diagnosis and prognosis, are often clinically constrained. To address this, we developed the kidney intelligent diagnosis system (KIDS), a noninvasive model for renal biopsy prediction using 13,144 retinal images from 6773 participants. The KIDS achieves an area under the receiver operating characteristic curve (AUC) of 0.839-0.993 for CKD screening and accurately identifies the five most common pathological types (AUC: 0.790-0.932) in a multicenter and multi-ethnic validation, outperforming nephrologists by 26.98% in accuracy. Additionally, the KIDS further predicts disease progression based on pathological classification. Given its flexible strategy, the KIDS can be adapted to local conditions to provide a tailored tool for patients. This noninvasive model has the potential to improve CKD clinical management, particularly for those who are ineligible for biopsies.

Fig. 1. A kidney intelligent diagnosis system (KIDS) based on retinal images and clinical data for noninvasive pathological diagnosis and prognosis prediction of chronic kidney disease.

A Illustration of the geographical distribution map of data sources used in the Kidney Intelligent Diagnosis System (KIDS). The development set, external and multicenter, and multi-ethnic real-world test datasets were collected from 9 hospitals. Data collection included retinal images, renal biopsy, and laboratory and ultrasound examinations. B We used retinal images to develop deep learning models for CKD screening. Combined with nephropathy examinations, a multimodal model was developed to predict 5 renal pathology classifications. Then, based on the predicted renal pathology, prognosis predictions were made for CKD patients. AI artificial intelligence, ZOC Zhongshan Ophthalmic Center of Sun Yat-sen University, FAH First Affiliated Hospital of Sun Yat-sen University, ZPH Zhongshan City People’s Hospital, FPH First People’s Hospital of Foshan, AHY Affiliated Hospital of Youjiang Medical University for Nationalities, FPHK First People’s Hospital of Kashi, SPTCMI Shanxi Provincial Traditional Chinese Medicine Institute, SAH The Second Affiliated Hospital of Xi’an Jiaotong University, BH Banadir Hospital in Somalia. IgAN IgA nephropathy, MN idiopathic membranous nephropathy, DN diabetic nephropathy, ANS arterionephrosclerosis, MCD/FSGS idiopathic minimal change disease and focal segmental glomerulosclerosis.

Fig. 2. Performance of the CKD screening AI model using retinal images.

ROC curves showing the model’s performance in detecting CKD (a, c) and in classifying CKD stages (early, moderate, and advanced) (b, d) using retinal images alone. Results are shown for the internal test set (a, b) and the external test set (c, d). CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate. Early CKD, eGFR ≥ 60 mL/min/1.73 m²; moderate CKD, eGFR 30–59 mL/min/1.73 m²; advanced CKD, eGFR <30 mL/min/1.73 m². AUC area under the curve, CI confidence interval. Source data are provided as a Source Data file.

Fig. 3. Performance of the pathological diagnosis AI models in image-only, clinical data-only, and hybrid models.

ROC curves for the performance of pathological diagnosis of IgAN, MN, DN, ANS, and MCD/FSGS (a–e) in the internal test set, prospective test set, and external test set. DN diabetic nephropathy, IgAN IgA nephropathy, MN idiopathic membranous nephropathy, ANS arterionephrosclerosis, MCD/FSGS idiopathic minimal change disease and focal segmental glomerulosclerosis, AUC area under the curve, CI confidence interval. Source data are provided as a Source Data file.

Fig. 4. Progression of CKD in different pathological types and performance of Cox proportional hazards regression models in progression prediction.

a Kaplan−Meier curves for the progression of CKD according to different pathological types. IgAN IgA nephropathy, MN idiopathic membranous nephropathy, DN diabetic nephropathy, ANS arterionephrosclerosis, MCD/FSGS idiopathic minimal change disease and focal segmental glomerulosclerosis. b Kaplan−Meier curves for risk stratification from the Cox proportional hazards regression model with predicted pathological types from the image-only AI model. Survival curves represent the high-risk, medium-risk, and low-risk subgroups (risk score <Q1, Q1–Q3, >Q3), and 95% CI regions are represented as shaded areas around the curve. ROC curves showing the performance of Cox proportional hazards regression models at 1, 3, and 5 years with 10-fold cross-validation using c pathological types from renal biopsy and d predicted pathological types from the image-only AI model. The P value was calculated via the log-rank test among all groups. AUC area under the curve, CI confidence interval. Source data are provided as a Source Data file.

Fig. 5. Description of the clinical application scenarios for the KIDS and human and artificial intelligence comparison studies.

A The KIDS system can be applied in the following two clinical scenarios: scenario 1 involves CKD screening based on retinal images. When a participant arrives at a primary care setting, the KIDS can identify potential CKD cases from retinal images and recommend referrals for suspicious cases to specialized clinics, enabling early detection and timely intervention for CKD patients. Scenario 2: Noninvasive pathological diagnosis. In specialist nephrology clinics, the KIDS is expected to provide objective pathological diagnosis and predict adverse outcomes without the need for invasive renal biopsy. This information can assist nephrologists in making precise treatment decisions and managing patients with personalized care. B A human and artificial intelligence comparison study was performed to evaluate the performance of the KIDS compared with that of nephrologists in diagnosing pathology via ROC curves. C ROC curve plots revealed that the KIDS achieved greater sensitivity than did all the other nephrologists, except for the diagnosis of ANS, for which the sensitivities of the two experts were slightly greater than those of the KIDS. In terms of accuracy, KIDS also exhibited a greater and more stable advantage. KIDS kidney intelligent diagnosis system, ROC receiver operating characteristic, IgAN IgA nephropathy, MN idiopathic membranous nephropathy, DN diabetic nephropathy, ANS arterionephrosclerosis, MCD/FSGS idiopathic minimal change disease and focal segmental glomerulosclerosis. CN-R Chinese resident nephrologist, CN-S Chinese senior nephrologist, CN-E Chinese expert nephrologist, SO-R Somali resident nephrologist, SO-S Somali senior nephrologist, SO-E Somali expert nephrologist, SO-N Somali nephrologists. Source data are provided as a Source Data file.