Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

• Published in: BioMed research international Vol. 2020; no. 2020; pp. 1 - 14
• Main Authors: Covic, Adrian, Lupu, Paula Madalina, Popa, Iolanda Valentina, Jugrin, Daniel, Iftene, Adrian, Burlacu, Alexandru, Vlad, Cristiana
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Algorithms; Anemia; Artificial Intelligence; Cardiac arrhythmia; Clinical trials; Computational linguistics; Data mining; Decision Making, Computer-Assisted; Deep learning; Dialysis; Diet; FDA approval; Fistula; Graft Rejection; Health aspects; Hemodialysis; Hospital administration; Humans; Hypotension; Kidney diseases; Kidney Failure, Chronic - diagnosis; Kidney Failure, Chronic - physiopathology; Kidney Failure, Chronic - therapy; Kidney transplantation; Kidney Transplantation - adverse effects; Kidney Transplantation - statistics & numerical data; Kidney transplants; Kidneys; Language processing; Learning algorithms; Literature reviews; Machine learning; Management systems; Medical equipment; Models, Statistical; Natural language interfaces; Nephrology; Peritoneal dialysis; Peritoneum; Quality of Life; Renal Dialysis; Renal replacement therapy; Review; Software; Survival; Tacrolimus; Transplantation; Transplants & implants; Romania
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2020/9867872

Background. The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? Methods. We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. Results. AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. Conclusions. Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.