{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:03:12Z","timestamp":1785542592401,"version":"3.56.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"6","funder":[{"DOI":"10.13039\/100000738","name":"U.S. Department of Veterans Affairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000738","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>The global derangement of mineral metabolism that accompanies chronic kidney disease (CKD-MBD) is a major driver of the accelerated mortality for individuals with kidney disease. Advances in the delivery of dialysis, in the composition of phosphate binders, and in the therapies directed towards secondary hyperparathyroidism have failed to improve the cardiovascular event profile in this population. Many obstacles have prevented progress in this field including the incomplete understanding of pathophysiology, the lack of clinical targets for early stages of chronic kidney disease, and the remarkably wide diversity in clinical manifestations. We describe in this review a novel approach to CKD-MBD combining mathematical modelling of biologic processes with machine learning artificial intelligence techniques as a tool for the generation of new hypotheses and for the development of innovative therapeutic approaches to this syndrome. Clinicians need alternative targets of therapy, tools for risk profile assessment, and new therapies to address complications early in the course of disease and to personalize therapy to each individual. The complexity of CKD-MBD suggests that incorporating artificial intelligence techniques into the diagnostic, therapeutic, and research armamentarium could accelerate the achievement of these goals.<\/jats:p>","DOI":"10.1093\/ckj\/sfae143","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T07:37:54Z","timestamp":1717745874000},"source":"Crossref","is-referenced-by-count":7,"title":["Application of artificial intelligence to chronic kidney disease mineral bone disorder"],"prefix":"10.1093","volume":"17","author":[{"given":"Eleanor D","family":"Lederer","sequence":"first","affiliation":[{"name":"VA North Texas Health Care Services , Dallas TX , USA"},{"name":"Department of Medicine and Charles and Jane Pak Center for Mineral Metabolism and Clinical Research, UT Southwestern Medical Center , Dallas, TX , USA"},{"name":"Department of Medicine, University of Louisville Health Sciences Center , Louisville, KY , 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