Evidence map›Paper›PMID 36358448›Full record

ArticleBrain sciences2022

Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach.

Arttu Ruohola, Eero Salli, Timo Roine, Anna Tokola, Minna Laine, Ritva Tikkanen, Sauli Savolainen, Taina Autti

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Arttu RuoholaHUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00290 Helsinki, Finland.
Eero SalliHUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00290 Helsinki, Finland.ORCID 0000-0002-8797-6094
Timo RoineHUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00290 Helsinki, Finland.ORCID 0000-0002-3104-9734
Anna TokolaHUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00290 Helsinki, Finland.ORCID 0000-0002-5135-2191
Minna LaineDepartment of Child Neurology, Helsinki University Hospital and Helsinki University, P.O Box 900, FI-01400 Vantaa, Finland.ORCID 0000-0003-3337-3500
Ritva TikkanenInstitute of Biochemistry, Medical Faculty, University of Giessen, Friedrichstrasse 24, 35390 Giessen, Germany.ORCID 0000-0002-8393-1825
Sauli SavolainenHUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00290 Helsinki, Finland.ORCID 0000-0001-8085-322X
Taina AuttiHUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00290 Helsinki, Finland.

Funding

Helsinki University Hospital M780021002Helsinki University Hospital TYH2019253Helsinki University Hospital TYH2021229
6 · The paper itself

Abstract

Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients (

Indexed as

aspartylglucosaminuriaclassificationlysosomal storage disordersmagnetic resonance imagingsupervised learningthalamus

Identifiers

PMID36358448
PMCPMC9688716

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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.