Evidence map›Paper›PMID 40764795›Full record

ArticleScientific reports2025

A novel approach to smart-assisted schizophrenia screening based on Raman spectroscopy and deep learning.

Meng Xiao, Sulidan Xiaokaiti, Meng Shang, Pan Xu, Xiaofen Zhu

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Meng Xiao *Quzhou KeCheng People's Hospital, Quzhou, China.
Sulidan Xiaokaiti *The Fourth People's Hospital of Urumqi, Urumqi, China.
Meng ShangQuzhou KeCheng People's Hospital, Quzhou, China.
Pan XuQuzhou KeCheng People's Hospital, Quzhou, China.
Xiaofen ZhuQuzhou KeCheng People's Hospital, Quzhou, China. qzzhuxiaofen@163.com.

Funding

Science and Technology Program Project of Urumqi Health Commission 202123Zhejiang Province Medicine and Health Science and Technology Program 2024XY087
6 · The paper itself

Abstract

In this study, serum Raman spectra are introduced into the screening of schizophrenia. We collect serum Raman spectra from schizophrenic and healthy individuals, classified them based on four convolutional neural networks, and developed an assisted screening method for schizophrenia based on serum Raman spectra. We also introduce Markov transition field (MTF), which is commonly used in time-series signal processing, into Raman spectral analysis, and convert 1D Raman spectral sequences into 2D spectrograms to enrich the method of Raman spectral analysis. The experimental results show that the performance of the model trained based on MTF is overall better than that of the model trained based on 1D spectral sequences.

Indexed as

Deep LearningSchizophreniaSpectrum Analysis, RamanAdultFemaleHumansMaleMarkov ChainsNeural Networks, ComputerDeep learningMedical diagnoseRaman spectroscopySchizophrenia

Identifiers

PMID40764795
PMCPMC12325642

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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.