ArticleBrain communications2023
Headache classification and automatic biomarker extraction from structural MRIs using deep learning.
Article in Brain communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Application of machine learning in migraine classification: a call for study design standardization and global collaboration.The journal of headache and pain · 2025Pooled it
- Artificial intelligence in headache medicine: between automation and the doctor-patient relationship. A systematic review.The journal of headache and pain · 2025Pooled it
- Biomarkers in headaches as a potential solution to simplify differential diagnosis of primary headache disorders: a systematic review.The journal of headache and pain · 2025Pooled it
- Structural Magnetic Resonance Imaging of Post-Traumatic Headache: A Systematic Review.Current pain and headache reports · 2025Pooled it
- Machine Learning Classification of Migraine Using fNIRS During a Postural Task.Brain sciences · 2026Article
- AUCp: Pseudo-AUC for Inference Model Selection With Unlabeled Validation Data in Abnormality Detection.IEEE transactions on medical imaging · 2026Article
- End-to-End Deep Learning Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Patients Using Routine MRI.Journal of imaging informatics in medicine · 2025Article
- Leveraging multi-modal foundation model image encoders to enhance brain MRI-based headache classification.Scientific reports · 2025Article
- A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data.The journal of headache and pain · 2025Article
- Artificial intelligence terminology, methodology, and critical appraisal: A primer for headache clinicians and researchers.Headache · 2025Review
- Current and prospective roles of magnetic resonance imaging in mild traumatic brain injury.Brain communications · 2025Review
- Migraine triggers, phases, and classification using machine learning models.Frontiers in neurology · 2025Article
- Application of Artificial Intelligence in the Headache Field.Current pain and headache reports · 2024Review
- Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.The journal of headache and pain · 2024Review
- Connectivity of the insular subdivisions differentiates posttraumatic headache-associated from nonheadache-associated mild traumatic brain injury: an arterial spin labelling study.The journal of headache and pain · 2024Article
- Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
- Developing an Artificial Intelligence-Based Pediatric and Adolescent Migraine Diagnostic Model.Cureus · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Data-driven machine-learning methods on neuroimaging (e.g. MRI) are of great interest for the investigation and classification of neurological diseases. However, traditional machine learning requires domain knowledge to delineate the brain regions first, followed by feature extraction from the regions. Compared with this semi-automated approach, recently developed deep learning methods have advantages since they do not require such prior knowledge; instead, deep learning methods can automatically find features that differentiate MRIs from different cohorts. In the present study, we developed a deep learning-based classification pipeline distinguishing brain MRIs of individuals with one of three types of headaches [migraine (
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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.