Table 1:
Examples of AI tools in nephrology.
| Field | Study | Scenario | Purpose | Algorithm | Performance | Limitations |
|---|---|---|---|---|---|---|
| AKI | Postop-MAKE [24] | Prediction: patients with normal renal function undergoing cardiac surgery with cardiopulmonary bypass |
Prediction model based on nine preoperative variables (clinical, laboratory, imaging) that predict the risk of developing AKI after surgery | Nanogram. Logistic regression performed with variables selected using LASSO | High discriminatory power: AUC of 0.740 (95% CI 0.726–0.753) in the validation group |
Single-center retrospective study; treatment protocols for these patients could vary from center to center |
| STARZ [26] | Prediction: neonates admitted to NICU Multicenter, prospective |
Prediction model to assess the risk of AKI development in neonates (≤28 days) admitted to NICU on fluids for at least 48 h. Based on 10 variables | Multivariable logistic regression with stepwise backward elimination method | High discriminatory power: the model had an AUC of 0.974 (95% CI 0.958–0.990) |
No cystatin C access to evaluate kidney function in neonates Multicenter but in one single country |
|
| Tomašev et al. (2019) [39] | Prediction: multisite retrospective dataset of 703 782 adult patients (US Department of Veterans Affairs) |
Prediction model developed to calculate the probability of AKI occurring at any stage of severity within the next 48 h. Variables extracted from EHR | Recurrent neural network Deep learning |
Prediction of 55.8% of inpatient episodes of AKI and 90.2% of AKI requiring dialysis with a lead time of up to 48 h. Ratio of two false alerts for every true alert | 90% men Urine output not included Retrospective; no external validation |
|
| Zhang et al. (2019) [35] | Prediction/treatment: AKI patients (n = 6682) with urine output <0.5 mL/kg/h during first 6 h after ICU admission and fluid intake >5 L (US-based critical care database) |
Prediction model used to differentiate between volume-responsive and volume-unresponsive AKI | XGBoost algorithm | AUC 0.860 (95% CI 0.842–0.878) | No external validation Only measurement of short-term outcomes |
|
| CKD | Chan et al. (2021) [40] | Prediction: patients (n = 1146) with prevalent DKD (G3a–G3b with all grades of albuminuria (A1–A3) and G1 and G2 with A2–A3 level albuminuria) |
Prediction risk of ESKD in patients with CKD and type 2 diabetes mellitus Combination of novel biomarkers and data extracted from EHR (KidneyIntelX score). Comparison with traditional models |
ML Random forest model |
AUC for composite kidney endpoint 0.77 compared with an AUC of 0.61 (95% CI 0.60–0.63) for the clinical model | Missing data for urine results |
| Hermsen et al. (2019) [41] | Diagnosis: use of kidney transplant biopsies and nephrectomy samples |
Multiclass segmentation of digitized kidney tissue sections Facilitation of the histological study of samples using digital analysis and comparison of this approach with the performance of expert pathologists |
Deep learning: CNN | The best segmented class was glomeruli. The mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94 | This work focused only on the segmentation of glomeruli and refers to a small sample size | |
| Glomerular disease | IgAN-tool (Asia) [22] | Prediction: patients with IgAN from multiple centers in China (n = 2047) Multicenter, retrospective |
ESKD prediction for IgAN patients Model based on 10 clinical, laboratory, and histological variables |
XGBoost algorithm | High discriminatory power: C-statistic of 0.84 (95% CI 0.80–0.88) for the validation cohort | Study only performed in Asian patients |
| IgAN-tool (EU) [42] | Prediction: IgAN patients (n = 948). Retrospective. Follow-up 89 months |
ESKD prediction for IgAN patients Model based on seven clinical and histological variables including MEST-C score |
Cox regression for variable selection DL: ANN |
AUC 0.82 at 5 years AUC 0.89 at 10 years |
Developed and tested in retrospective cohorts Therapeutic interventions not included |
|
| Inherited kidney disease | Jefferies et al. (2021) [43] | Diagnosis: de-identified health records from a cohort of patients with confirmed Fabry disease (n = 4978) Patients from 50 US states |
Diagnosis of Fabry disease using EHR. ICD-10 codes used AI tool (OM1 Patient FinderTM) (OM1 Inc., Boston, MA) |
ML (model not specified) | AUROC 0.82 Testing in males only: AUROC 0.83 Testing in females only: AUROC 0.82 |
Missings in health records Gender imbalance Not validated in external cohorts outside the USA |
| Potretzke et al. (2023) [18] | Diagnosis/prediction: Patients with ADPKD undergoing MR imaging between November 2019 and January 2021 (N = 170) 1 center: Mayo Clinic |
Evaluate performance in clinical practice of an AI algorithm for MR-derived total kidney volume in ADPKD | DL: CNN | AI algorithm TKV output mean volume difference –3.3%. Agreement for disease class between AI-based and manually edited segmentation high (five cases differed) | Prospective study in different centers to evaluate whether the algorithm is generalizable remains to be elucidated | |
| Dialysis | Barbieri et al. (2016) [34] | Treatment: hemodialysis patients (n = 752) in three different NephroCare centers (Fresenius Medical Care network) across the EU |
Anemia control model to recommend suitable erythropoietic-stimulating agent doses based on patient profiles | DL: ANN | Hb SD decreased (0.97 ± 0.41 g/dL to 0.8 ± 0.29 g/dL) Hb within target 84.1% vs 64.5% |
Not a randomized or blinded controlled trial Short follow-up period to assess outcomes No external validation |
| Zhang et al. (2022) [44] | Diagnosis: digital images of AV accesses before cannulation (1425 AV access images) Cohort of hemodialysis patients from 20 dialysis clinics across six US states |
Classification of vascular access aneurysm as “non-advanced” or “advanced” | DL: CNN | AUROC 0.96 | Real world testing in a demographically diverse population remains to be published Clinical parameters not included |
|
| Lee et al. (2021) [45] | Prediction: analysis of 261 647 hemodialysis sessions (N = 9292) One center (Seoul National University Hospital) |
DL model to predict the risk of intradialytic hypotension using a timestamp-bearing dataset | DL: RNN | AUC 0.94 for prediction of intradialytic hypotension 1 (defined as nadir systolic BP <90 mmHg) | Retrospective cohort. One center Other factors not included (cardiac monitoring, dialysis vintage and medical records) |
|
| Kidney transplant | IBox [23] | Prediction: kidney transplant recipients (n = 7557) from 10 medical centers across Europe and USA |
Prediction of allograft failure Eight functional, histological and immunological prognostic factors combined into a risk score |
Cox regression with boot-strapping for validation | C index 0.18 (95% CI 0.79–0.83) Validation cohorts: Europe: C index 0.81 (95% CI 0.78–0.84) US: C index 0.80 (95% CI 0.76–0.84) |
Emerging predictors post-transplant missing. Adherence not taken into account. Validation in daily clinical practice remains to be analyzed |
ADPKD, autosomal dominant polycystic kidney disease; ANN, artificial neural network; AUC, area under the curve; AUROC, area under the receiver operating characteristic curve; AV access, arteriovenous access; BP, blood pressure; CI: confidence interval; CNN, convolutional neural network; DKD, diabetic kidney disease; ESKD, end-stage kidney disease; Hb, hemoglobin; ICD, International Classification of Diseases; ICU, intensive care unit; IgAN, immunoglobulin A nephropathy; LASSO, least absolute shrinkage and selection operator; MR, magnetic resonance; NICU, neonatal intensive care unit; RNN, recurrent neural network; SD, standard deviation; TKV, total kidney volume; XGBoost, extreme gradient boosting.