Prediction of chronic kidney disease and its progression by artificial intelligence algorithms

• Published in: Journal of nephrology Vol. 35; no. 8; pp. 1953 - 1971
• Main Authors: Schena, Francesco Paolo, Anelli, Vito Walter, Abbrescia, Daniela Isabel, Di Noia, Tommaso
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.11.2022
• Subjects: Algorithms; Artificial Intelligence; Databases, Factual; Disease Progression; Humans; Machine Learning; Medicine; Medicine & Public Health; Nephrology; Renal Insufficiency, Chronic - diagnosis; Systematic Reviews; Urology; Chronic kidney disease; Machine learning models; Artificial intelligence; Renal failure; Prediction
• ISSN: 1724-6059; 1724-6059
• DOI: 10.1007/s40620-022-01302-3

Background and objective Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitioners and nephrologists are involved in the first and in the late stages of the disease, respectively. Early diagnosis of CKD is an important step in preventing the progression of kidney damage. Our aim was to review publications on machine learning algorithms (MLAs) that can predict early CKD and its progression. Methods We conducted a systematic review and selected 55 articles on the application of MLAs in CKD. PubMed, Medline, Scopus, Web of Science and IEEE Xplore Digital Library of the Institute of Electrical and Electronics Engineers were searched. The search terms were chronic kidney disease, artificial intelligence, data mining and machine learning algorithms. Results MLAs use enormous numbers of predictors combining them in non-linear and highly interactive ways. This ability increases when new data is added. We observed some limitations in the publications: (i) databases were not accurately reviewed by physicians; (ii) databases did not report the ethnicity of the patients; (iii) some databases collected variables that were not important for the diagnosis and progression of CKD; (iv) no information was presented on the native kidney disease causing CKD; (v) no validation of the results in external independent cohorts was provided; and (vi) no insights were given on the MLAs that were used. Overall, there was limited collaboration among experts in electronics, computer science and physicians. Conclusions The application of MLAs in kidney diseases may enhance the ability of clinicians to predict CKD and RF, thus improving diagnostic assistance and providing suitable therapeutic decisions. However, it is necessary to improve the development process of MLA tools.