Evidence map›Paper›PMID 30815458›Full record

ReviewKidney diseases (Basel, Switzerland)2019

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

Miguel Hueso, Estanislao Navarro, Diego Sandoval, Josep Maria Cruzado

Open access · goldAbstract readReview
In one paragraph

Review in Kidney diseases (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.6field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 45 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Review
  9. Role of Artificial Intelligence in Kidney Disease.International journal of medical sciences · 2020
    Review
  10. Artificial Intelligence and Dialysis.Kidney diseases (Basel, Switzerland) · 2019
    Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Miguel HuesoNephrology Department, Hospital Universitari Bellvitge and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain.
Estanislao NavarroIndependent Researcher, Barcelona, Spain.
Diego SandovalNephrology Department, Hospital Universitari Bellvitge and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain.
Josep Maria CruzadoNephrology Department, Hospital Universitari Bellvitge and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain.
Bellvitge University Hospital · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRenal 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.

Indexed as

Artificial intelligenceArtificial kidneyHemodialysisMachine learning

Identifiers

PMID30815458
PMCPMC6388440
OpenAlexW2896819547

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.