Evidence map›Paper›PMID 37489153›Full record

ArticleHealth information science and systems2023

Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review.

Guan Huang, Renjie Li, Quan Bai, Jane Alty

Registry-linked trialAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Health information science and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06114914 (TapTalkTest), which is not on this map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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.

NCT06114914 unknown statusnot on this map

TapTalkTest: Development of a Non-invasive Screening Test to Detect Risk of Alzheimer's Disease Pathology

TypeobservationalSponsorUniversity of TasmaniaRan2022 to 2025Enrolled1,000ConditionsAlzheimer Disease, Dementia, Movement Abnormalities, Speech DisordersArmsTap Talk online program
3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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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

4 authors.

Guan HuangSchool of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia.
Renjie LiSchool of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia.
Quan BaiSchool of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia.
Jane AltyWicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS 7000 Australia.ORCID 0000-0002-5456-8676

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With ageing populations around the world, there is a rapid rise in the number of people with Alzheimer's disease (AD) and Parkinson's disease (PD), the two most common types of neurodegenerative disorders. There is an urgent need to find new ways of aiding early diagnosis of these conditions. Multimodal learning of clinically accessible data is a relatively new approach that holds great potential to support early precise diagnosis. This scoping review follows the PRSIMA guidelines and we analysed 46 papers, comprising 11,750 participants, 3569 with AD, 978 with PD, and 2482 healthy controls; the recency of this topic was highlighted by nearly all papers being published in the last 5 years. It highlights the effectiveness of combining different types of data, such as brain scans, cognitive scores, speech and language, gait, hand and eye movements, and genetic assessments for the early detection of AD and PD. The review also outlines the AI methods and the model used in each study, which includes feature extraction, feature selection, feature fusion, and using multi-source discriminative features for classification. The review identifies knowledge gaps around the need to validate findings and address limitations such as small sample sizes. Applying multimodal learning of clinically accessible tests holds strong potential to aid the development of low-cost, reliable, and non-invasive methods for early detection of AD and PD.

Indexed as

Age-related diseasesAlzheimer’s diseaseArtificial intelligenceDiagnosisMultimodal learningMultiple biomarkersParkinson’s diseasePre-clinical

Identifiers

PMID37489153
PMCPMC10363100

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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 Socratic graph.