Evidence map›Paper›PMID 30498877›Full record

ReviewJapanese journal of radiology2019

Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

Koji Sakai, Kei Yamada

Abstract readReview
PubMed Publisher
In one paragraph

Review in Japanese journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 3 pooled it
–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

53 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  13. Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023
    Review
  14. Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023
    Article
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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

2 authors.

Koji SakaiDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kajii-cho, Kawaramachi Hirokoji Agaru, Kamigyo-ku, Kyoto, Kyoto, 602-8566, Japan. sakai3@koto.kpu-m.ac.jp.
Kei YamadaDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kajii-cho, Kawaramachi Hirokoji Agaru, Kamigyo-ku, Kyoto, Kyoto, 602-8566, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic lesion changes within the area of neuroradiology. However, to date, the majority of research trend and current status have not been clearly illuminated in the neuroradiology field. More than 1000 papers have been published during the past 5 years on subject classification and prediction focused on multiple brain disorders. We provide a survey of 209 papers in this field with a focus on top ten active areas of research; i.e., Alzheimer's disease/mild cognitive impairment, brain tumor; schizophrenia, depressive disorders, Parkinson's disease, attention-deficit hyperactivity disorder, autism spectrum disease, epilepsy, multiple sclerosis, stroke, and traumatic brain injury. Detailed information of these studies, such as ML methods, sample size, type of inputted features and reported accuracy, are summarized. This paper reviews the evidences, current limitations and status of studies using ML to assess brain disorders in neuroimaging data. The main bottleneck of this research field is still the limited sample size, which could be potentially addressed by modern data sharing models, such as ADNI.

Indexed as

Machine LearningBrainBrain DiseasesHumansNeuroimagingArtificial intelligenceDiagnosisMachine learningNeuroimagingNeurological disorder

Identifiers

PMID30498877

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.