Evidence map›Paper›PMID 41234455›Full record

ReviewTurkish journal of medical sciences2025

Current insights on predicting vestibular diseases using machine learning.

Emre Söylemez, Muhammed Mustafa Şeker

Abstract readReview
In one paragraph

Review in Turkish journal of medical sciences, 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
–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

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

2 authors.

Emre SöylemezDepartment of Audiometry, Vocational School of Health Services, Karabük University, Karabük, Turkiye.ORCID https://orcid.org/0000-0002-7554-3048
Muhammed Mustafa ŞekerDepartment of Audiology and Speech Pathology, Institute of Health Sciences, Ankara University, Ankara, Turkiye.ORCID https://orcid.org/0000-0001-8517-2925

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The vestibular system is one of the three main systems responsible for maintaining balance and posture. Accurate vestibular inputs enable the perception of the head's position and movement in space, and ensure coordination between head movements, eye movements, balance, and posture. Any dysfunction in the peripheral vestibular end organs, the vestibular nerve, or the central vestibular system may lead to vertigo, dizziness, and gait disturbances in individuals. Some syndromes that cause vertigo symptoms can be life threatening. Although peripheral vestibular pathologies are generally benign, they can reduce patients' quality of life, cause falls, and hinder independence. Therefore, the diagnosis and management of vestibular disorders are of great importance. However, due to the complex structure of the vestibular system and the complexity of its symptoms, some vestibular diseases may go undiagnosed or be misdiagnosed. Machine learning (ML), a subfield of artificial intelligence, enables computer systems to learn patterns and relationships from data and make predictions or decisions. The growing capabilities of ML in data processing combined with the needs of healthcare, offer significant opportunities in early diagnosis of diseases, treatment planning, and personalization of healthcare services. The present review provides a general overview of the prediction of vestibular disorders using ML.

Indexed as

Diagnosis, Computer-AssistedMachine LearningVestibular DiseasesVestibular SystemDiagnostic ErrorsEye Movement MeasurementsGait AnalysisHumansPsychometricsBPPVMachine learningMénière’s diseasevestibular diseasesvestibular neuritis

Identifiers

PMID41234455
PMCPMC12611390

What Socratic 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 Socratic graph.