Evidence map›Paper›PMID 35808481›Full record

ReviewSensors (Basel, Switzerland)2022

Machine Learning for Renal Pathologies: An Updated Survey.

Roberto Magherini, Elisa Mussi, Yary Volpe, Rocco Furferi, Francesco Buonamici, Michaela Servi

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
  2. Article
  3. Efficacy of Dynamics-based Features for Machine Learning Classification of Renal Hemodynamics.Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) · 2023
    Article
  4. 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

6 authors at 1 institution in 1 country.

Roberto MagheriniDepartment of Industrial Engineering, University of Florence, 50139 Florence, Italy.ORCID 0000-0002-4606-5251
Elisa MussiDepartment of Industrial Engineering, University of Florence, 50139 Florence, Italy.ORCID 0000-0002-5170-3246
Yary VolpeDepartment of Industrial Engineering, University of Florence, 50139 Florence, Italy.ORCID 0000-0002-5668-1912
Rocco FurferiDepartment of Industrial Engineering, University of Florence, 50139 Florence, Italy.ORCID 0000-0001-6771-5981
Francesco BuonamiciDepartment of Industrial Engineering, University of Florence, 50139 Florence, Italy.ORCID 0000-0001-5186-9724
Michaela ServiDepartment of Industrial Engineering, University of Florence, 50139 Florence, Italy.ORCID 0000-0002-4071-6615
University of Florence · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

Indexed as

KidneyMachine LearningDatabases, Factualartificial intelligencedeep learningmachine learningrenal pathology

Identifiers

PMID35808481
PMCPMC9269842
OpenAlexW4283819183

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.