Literature DB >> 30815458

Progress in the Development and Challenges for the Use of Artificial Kidneys and Wearable Dialysis Devices.

Miguel Hueso1, Estanislao Navarro2, Diego Sandoval1, Josep Maria Cruzado1.   

Abstract

BACKGROUND: Renal transplantation is the treatment of choice for chronic kidney disease (CKD) patients, but the shortage of kidneys and the disabling medical conditions these patients suffer from make dialysis essential for most of them. Since dialysis drastically affects the patients' lifestyle, there are great expectations for the development of wearable artificial kidneys, although their use is currently impeded by major concerns about safety. On the other hand, dialysis patients with hemodynamic instability do not usually tolerate intermittent dialysis therapy because of their inability to adapt to a changing scenario of unforeseen events. Thus, the development of novel wearable dialysis devices and the improvement of clinical tolerance will need contributions from new branches of engineering such as artificial intelligence (AI) and machine learning (ML) for the real-time analysis of equipment alarms, dialysis parameters, and patient-related data with a real-time feedback response. These technologies are endowed with abilities normally associated with human intelligence such as learning, problem solving, human speech understanding, or planning and decision-making. Examples of common applications of AI are visual perception (computer vision), speech recognition, and language translation. In this review, we discuss recent progresses in the area of dialysis and challenges for the use of AI in the development of artificial kidneys. SUMMARY AND KEY MESSAGES: Emerging technologies derived from AI, ML, electronics, and robotics will offer great opportunities for dialysis therapy, but much innovation is needed before we achieve a smart dialysis machine able to analyze and understand changes in patient homeostasis and to respond appropriately in real time. Great efforts are being made in the fields of tissue engineering and regenerative medicine to provide alternative cell-based approaches for the treatment of renal failure, including bioartificial renal systems and the implantation of bioengineered kidney constructs.

Entities:  

Keywords:  Artificial intelligence; Artificial kidney; Hemodialysis; Machine learning

Year:  2018        PMID: 30815458      PMCID: PMC6388440          DOI: 10.1159/000492932

Source DB:  PubMed          Journal:  Kidney Dis (Basel)        ISSN: 2296-9357


  33 in total

1.  Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy.

Authors:  A I Akl; M A Sobh; Y M Enab; J Tattersall
Journal:  Am J Kidney Dis       Date:  2001-12       Impact factor: 8.860

2.  Clinical evaluation of an expert system for arteriovenous fistula assessment.

Authors:  Jacques Chanliau; Christophe Charasse; Cédric Rose; Bernard Béné
Journal:  Int J Artif Organs       Date:  2014-12-02       Impact factor: 1.595

Review 3.  Proximal tubule function and response to acidosis.

Authors:  Norman P Curthoys; Orson W Moe
Journal:  Clin J Am Soc Nephrol       Date:  2013-08-01       Impact factor: 8.237

4.  Production and implantation of renal extracellular matrix scaffolds from porcine kidneys as a platform for renal bioengineering investigations.

Authors:  Giuseppe Orlando; Alan C Farney; Samy S Iskandar; Sayed-Hadi Mirmalek-Sani; David C Sullivan; Emma Moran; Tamer AbouShwareb; Paolo De Coppi; Kathryn J Wood; Robert J Stratta; Anthony Atala; James J Yoo; Shay Soker
Journal:  Ann Surg       Date:  2012-08       Impact factor: 12.969

5.  Blood volume controlled hemodialysis in hypotension-prone patients: a randomized, multicenter controlled trial.

Authors:  Antonio Santoro; Elena Mancini; Carlo Basile; Luigi Amoroso; Salvatore Di Giulio; Mario Usberti; Giuliano Colasanti; Giuseppe Verzetti; Alessandro Rocco; Enrico Imbasciati; Giovanni Panzetta; Roberto Bolzani; Fabio Grandi; Maurizio Polacchini
Journal:  Kidney Int       Date:  2002-09       Impact factor: 10.612

Review 6.  Cell-based strategies for the treatment of kidney dysfunction: a review.

Authors:  Christopher J Pino; Alexander S Yevzlin; James Tumlin; H David Humes
Journal:  Blood Purif       Date:  2012-10-24       Impact factor: 2.614

Review 7.  The bioartificial kidney: current status and future promise.

Authors:  H David Humes; Deborah Buffington; Angela J Westover; Shuvo Roy; William H Fissell
Journal:  Pediatr Nephrol       Date:  2013-04-26       Impact factor: 3.714

Review 8.  Computational thinking and thinking about computing.

Authors:  Jeannette M Wing
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2008-10-28       Impact factor: 4.226

Review 9.  The bioartificial kidney in the treatment of acute kidney injury.

Authors:  Joon Ho Song; H David Humes
Journal:  Curr Drug Targets       Date:  2009-12       Impact factor: 3.465

10.  A selective cytopheretic inhibitory device to treat the immunological dysregulation of acute and chronic renal failure.

Authors:  H David Humes; Joseph T Sobota; Feng Ding; Joon Ho Song
Journal:  Blood Purif       Date:  2010-01-08       Impact factor: 2.614

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  4 in total

Review 1.  Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.

Authors:  Alexandru Burlacu; Adrian Iftene; Daniel Jugrin; Iolanda Valentina Popa; Paula Madalina Lupu; Cristiana Vlad; Adrian Covic
Journal:  Biomed Res Int       Date:  2020-06-10       Impact factor: 3.411

Review 2.  Role of Artificial Intelligence in Kidney Disease.

Authors:  Qiongjing Yuan; Haixia Zhang; Tianci Deng; Shumei Tang; Xiangning Yuan; Wenbin Tang; Yanyun Xie; Huipeng Ge; Xiufen Wang; Qiaoling Zhou; Xiangcheng Xiao
Journal:  Int J Med Sci       Date:  2020-04-06       Impact factor: 3.738

3.  Fluid and hemodynamic management in hemodialysis patients: challenges and opportunities.

Authors:  Bernard Canaud; Charles Chazot; Jeroen Koomans; Allan Collins
Journal:  J Bras Nefrol       Date:  2019 Oct-Dec

Review 4.  Drug-Induced Nephrotoxicity Assessment in 3D Cellular Models.

Authors:  Pengfei Yu; Zhongping Duan; Shuang Liu; Ivan Pachon; Jianxing Ma; George P Hemstreet; Yuanyuan Zhang
Journal:  Micromachines (Basel)       Date:  2021-12-21       Impact factor: 2.891

  4 in total

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