ArticleiScience2023
Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses 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
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The application of machine learning in the prediction of sports injuries: systematic review and meta-analysis.Scientific reports · 2026Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- A structure-aware and causal-invariant framework for glioma cell classification in pathological images.Frontiers in cell and developmental biology · 2026Article
- From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.Neurosurgical review · 2025Article
- Computed Tomography-Image-Based Glioma Grading Using Radiomics and Machine Learning: A Proof-of-Principle Study.Cancers · 2025Article
- Machine learning-based prediction of glioma grading.PloS one · 2025Article
- XAI-MRI: an ensemble dual-modality approach for 3D brain tumor segmentation using magnetic resonance imaging.Frontiers in artificial intelligence · 2025Article
- Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions.Sensors (Basel, Switzerland) · 2023Article
- Clinical application of machine-based deep learning in patients with radiologically presumed adult-type diffuse glioma grades 2 or 3.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to evaluate deep learning (DL) performance in differentiating low- and high-grade glioma. Search online database for studies continuously published from 1st January 2015 until 16th August 2022. The random-effects model was used for synthesis, based on pooled sensitivity (SE), specificity (SP), and area under the curve (AUC). Heterogeneity was estimated using the Higgins inconsistency index (I
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.