Evidence map›Paper›PMID 35336842›Full record

ReviewBiology2022

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders.

Aklima Akter Lima, M Firoz Mridha, Sujoy Chandra Das, Muhammad Mohsin Kabir, Md Rashedul Islam, Yutaka Watanobe

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 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

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  5. Smart assistive technologies for neurodisorders: A review on AI, IoT, and wearable systems for enhanced patient care.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
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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.

Aklima Akter LimaDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.ORCID 0000-0002-0807-523X
M Firoz MridhaDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.ORCID 0000-0001-5738-1631
Sujoy Chandra DasDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.ORCID 0000-0002-3652-8769
Muhammad Mohsin KabirDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.ORCID 0000-0001-9624-5499
Md Rashedul IslamDepartment of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh.ORCID 0000-0001-8676-6338
Yutaka WatanobeDepartment of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan.ORCID 0000-0002-0030-3859

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.

Indexed as

challenges and opportunitiescomputer-aided diagnosis (CAD)deep learning (DL)detection and classificationmachine learning (ML)neurological disorders (NDs)

Identifiers

PMID35336842
PMCPMC8945195

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.