ArticleNeural plasticity2019
Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning.
Article in Neural plasticity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Consensus Statements on the Definition, Classification, and Diagnostic Tests for Tinnitus: A Delphi Study Conducted by the Korean Tinnitus Study Group.Journal of Korean medical science · 2024Pooled it
- Whole-brain functional activity and connectivity for the classification of subjective tinnitus: a machine learning study.Frontiers in neurology · 2026Article
- Application of neurodegenerative disease treatment strategies in tinnitus: mechanisms, translation, and prospects.Frontiers in aging neuroscience · 2026Review
- Consensus Statements on Tinnitus Assessment and Treatment Outcome Evaluation: A Delphi Study by the Korean Tinnitus Study Group.Journal of Korean medical science · 2025Article
- Explainable AI Method for Tinnitus Diagnosis via Neighbor-Augmented Knowledge Graph and Traditional Chinese Medicine: Development and Validation Study.JMIR medical informatics · 2024Article
- Increased fiber density of the fornix in patients with chronic tinnitus revealed by diffusion-weighted MRI.Frontiers in neuroscience · 2023Article
- Application of auditory mismatch negativity in tinnitus patients based on high-resolution electroencephalogram signals.Translational neuroscience · 2022Article
- A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics.Frontiers in digital health · 2021Review
- Neurobiology of Stress-Induced Tinnitus.Current topics in behavioral neurosciences · 2021Article
- Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery.Clinical and experimental otorhinolaryngology · 2020Review
- Pre-treatment structural brain biomarkers predict response to repetitive transcranial magnetic stimulation in subjective tinnitus.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphological neuroimaging biomarkers that may characterize idiopathic tinnitus using machine learning methods. Forty-six patients with idiopathic tinnitus and fifty-six healthy subjects were included in this study. For each subject, the gray matter volume of 61 brain regions was extracted as an original feature pool. From this feature pool, a hybrid feature selection algorithm combining the
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.