Evidence map›Paper›PMID 36140138›Full record

Trial reportBiosensors2022

The Feasibility of Early Alzheimer's Disease Diagnosis Using a Neural Network Hybrid Platform.

Xinke Yu, Siddharth Srivastava, Shan Huang, Eric Y Hayden, David B Teplow, Ya-Hong Xie

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
2.6field-weighted citation impact, top 9% 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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Xinke YuDepartment of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA.
Siddharth SrivastavaDepartment of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA.
Shan HuangDepartment of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0002-7375-5215
Eric Y HaydenDepartment of Neurology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0001-6977-5056
David B TeplowDepartment of Neurology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA.
Ya-Hong XieDepartment of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA.
University of California, Los Angeles · US

Funding

AFS/SERS Saliva-based SARS-CoV-2 Earliest Infection and Antibodies DetectionU18TR003778 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HUANG, TONY JUN, KIM, YONG · 2021 to 2022
$1.8M
Acoustofluidic Separation (AFS), Purification and Raman Spectral Fingerprinting of Single EVs: From Cell of Origin to Target Cell and BiofluidsUH3TR002978 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GALL, KENNETH A, HUANG, TONY JUN · 2021 to 2022
$1.8M
Acoustofluidic Separation (AFS), Purification and Raman Spectral Fingerprinting of Single EVs: From Cell of Origin to Target Cell and BiofluidsUG3TR002978 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AN, JI YEONG, GALL, KENNETH A · 2019 to 2020
$823k
NCATS NIH HHS U18 TR003778NCATS NIH HHS UG3 TR002978NCATS NIH HHS UH3 TR002978NIH HHS 1UG3TR002978-01
6 · The paper itself

Abstract

Early diagnosis of Alzheimer's Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research efforts have increasingly focused on using these computational methods to solve this challenge. Here, we demonstrate a convolutional neural network (CNN)-based AD diagnosis approach using the surface-enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). SERS and CNN were combined for biomarker detection to analyze disease-associated biochemical changes in the CSF. We achieved very high reproducibility in double-blind experiments for testing the feasibility of our system on human samples. We achieved an overall accuracy of 92% (100% for normal individuals and 88.9% for AD individuals) based on the clinical diagnosis. Further, we observed an excellent correlation coefficient between our test score and the Clinical Dementia Rating (CDR) score. Our findings offer a substantial indication of the feasibility of detecting AD biomarkers using the innovative combination of SERS and machine learning. We are hoping that this will serve as an incentive for future research in the field.

Indexed as

Alzheimer DiseaseBiomarkersEarly DiagnosisFeasibility StudiesHumansNeural Networks, ComputerReproducibility of ResultsBiomarkersAlzheimer’s diseasebiosensingdisease diagnosismachine learningmaterials sciencenanomaterialsneural networksRaman spectroscopySERS

Identifiers

PMID36140138
PMCPMC9496690
OpenAlexW4295845778

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.