ArticleBrain sciences2021
MRI-Based Radiomic Machine-Learning Model May Accurately Distinguish between Subjects with Internet Gaming Disorder and Healthy Controls.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Radiomics-based feature exploration and auxiliary diagnostic model construction for gaming disorder.Scientific reports · 2026Article
- Brain peptides in Alzheimer's disease - pathophysiology and therapeutic advances.Cell and tissue research · 2026Review
- Biomarkers of Internet Gaming Disorder-A Narrative Review.Journal of clinical medicine · 2024Review
- Application of eye and hand interventions in brain magnetic resonance imaging of young children.Heliyon · 2024Article
- Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning.Journal of behavioral addictions · 2023Article
- Radiomics; A Potential Next "Omics" in Psychiatric Disorders; An Introduction.Psychiatry investigation · 2023Article
- Abnormal psychological performance as potential marker for high risk of internet gaming disorder: An eye-tracking study and support vector machine analysis.Frontiers in psychology · 2022Article
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
Abstract
Purpose To identify cerebral radiomic features related to the diagnosis of Internet gaming disorder (IGD) and construct a radiomics-based machine-learning model for IGD diagnosis. Methods A total of 59 treatment-naïve subjects with IGD and 69 age- and sex-matched healthy controls (HCs) were recruited and underwent anatomic and diffusion-tensor magnetic resonance imaging (MRI). The features of the morphometric properties of gray matter and diffusion properties of white matter were extracted for each participant. After excluding the noise feature with single-factor analysis of variance, the remaining 179 features were included in an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power. Random forest classifiers were constructed and evaluated based on the identified features. Results No overall differences in the total brain volume (1,555,295.64 ± 152,316.31 mm
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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.