ReviewDiagnostics (Basel, Switzerland)2023
Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Pooled it
- Overcoming barriers to neurosurgical care with technology and innovation.BMC global and public health · 2026Review
- Machine learning in neuroradiology: Recent developments and applications.World journal of radiology · 2026Review
- A Comprehensive Review of Artificial Intelligence for Brain Tumor Analysis: Taxonomy, Robustness, and Open Challenges in Neuro-Oncology.Journal of imaging · 2026Review
- Using Machine Learning Methods to Predict Hospitalization Based on Brixia Score and Patient Clinical Data (from the COVID-19 Pandemic).Medicina (Kaunas, Lithuania) · 2026Article
- Motion artifacts and image quality in stroke MRI: associated factors and impact on AI and human diagnostic accuracy.European radiology · 2026Article
- Advances and uncertainties in fat embolism syndrome: a review.Trauma surgery & acute care open · 2026Review
- A deep learning based radiomics model for differentiating intraparenchymal hematoma induced by cerebral venous thrombosis.Scientific reports · 2025Article
- Review
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluation of Siemens Healthineers' StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke.Frontiers in neurology · 2025Article
- The Future of Academic Neuroradiology: Challenges, Opportunities, and Way Forward.AJNR. American journal of neuroradiology · 2024Article
- Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology.AJNR. American journal of neuroradiology · 2024Article
- The montage method improves the classification of suspected acute ischemic stroke using the convolution neural network and brain MRI.Radiological physics and technology · 2024Article
- Optimization of Radiology Diagnostic Services for Patients with Stroke in Multidisciplinary Hospitals.Materia socio-medica · 2024Review
- Leveraging Virtual Containers for High-Powered, Collaborative AI Research in Radiology.Journal of computer assisted tomographyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Identifiers
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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.