ReviewRadiologie (Heidelberg, Germany)2022
Machine learning in neuroimaging: from research to clinical practice.
Review in Radiologie (Heidelberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.Brain informatics · 2026Article
- Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.Statistics in medicine · 2026Article
- The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data.Scientific data · 2026Article
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- TC2-Res: a structured fusion of tract-level and connectome-level brain imaging in small-sample cohorts of athletes.Frontiers in neuroanatomy · 2026Article
- AI-Enabled Technologies and Biomarker Analysis for the Early Identification of Autism and Related Neurodevelopmental Disorders.Children (Basel, Switzerland) · 2025Review
- Multimodal contrastive learning on rs-fMRI to quantify whole-brain network recovery after hypothalamic hamartoma surgery.Biomedical engineering online · 2025Article
- Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods.Neuroinformatics · 2025Article
- The Role of Interleukin-8 (IL-8) in Treatment-Resistant Depression: A Review of Mechanisms, Biomarker Potential, and Therapeutic Implications.International journal of molecular sciences · 2025Review
- Multimodal investigation of the neurocognitive deficits underlying dyslexia in adulthood.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.Translational psychiatry · 2025Article
- Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Tetherless miniaturized point detector device for monitoring cortical surface hemodynamics in mice.Journal of biomedical optics · 2025Article
- Comparing the carbon footprint of fMRI data processing and analysis approaches.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Domain-Specific Prediction of Clinical Progression in Parkinson's Disease Using the Mosaic Approach.Brain and behavior · 2025Article
- Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?Neuroinformatics · 2024Review
- Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain.Clinical neuroradiology · 2024Review
- The Clinical Relevance of Artificial Intelligence in Migraine.Brain sciences · 2024Review
- Brainchop: Providing an Edge Ecosystem for Deployment of Neuroimaging Artificial Intelligence Models.Aperture neuro · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.
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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.