Evidence map›Paper›PMID 37974583›Full record

ArticleCognitive neurodynamics2023

Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression.

Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Parisa Moridian, Abbas Khosravi, Assef Zare, Juan M Gorriz, Amir Hossein Chale-Chale, Ali Khadem, U Rajendra Acharya

Open access · greenAbstract read
In one paragraph

Article in Cognitive neurodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 34% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

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  13. Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024
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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

10 authors at 8 institutions in 5 countries.

Afshin ShoeibiFPGA Lab, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.ORCID 0000-0003-0635-6799
Navid GhassemiComputer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran.
Marjane KhodatarsDepartment of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Parisa MoridianFaculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Abbas KhosraviInstitute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia.
Assef ZareFaculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran.
Juan M GorrizDepartment of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain.
Amir Hossein Chale-ChaleFaculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Ali KhademFaculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
U Rajendra AcharyaNgee Ann Polytechnic, Singapore, 599489 Singapore.
K.N.Toosi University of Technology · IRAsia University · TWDeakin University · AUFerdowsi University of Mashhad · IRIslamic Azad University, Mashhad · IRIslamic Azad University, Science and Research Branch · IRIslamic Azad University, Tehran · IRUniversidad de Granada · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.

Indexed as

ADHDCNN-AEDiagnosisfMRIGWOIT2FRSchizophrenia

Identifiers

PMID37974583
PMCPMC10640504
OpenAlexW4281625734

What Socratic 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 Socratic graph.