ReviewNeuroImage2023
Applications of generative adversarial networks in neuroimaging and clinical neuroscience.
Review in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis 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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- Comparative Evaluation of Deep Generative Models for Predicting 12-Month Neovascular AMD Progression Using OCT and Fundus Photography.Journal of imaging informatics in medicine · 2026Article
- MRI-to-PET synthesis via deep learning for amyloid-β quantification in Alzheimer's disease.European radiology · 2026Article
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Intelligent Manufacturing for Osteoarthritis Organoids.Cell proliferation · 2025Review
- From knowledge generation to knowledge verification: examining the biomedical generative capabilities of ChatGPT.iScience · 2025Article
- Pioneering new paths: the role of generative modelling in neurological disease research.Pflugers Archiv : European journal of physiology · 2025Review
- Generative AI as a tool to accelerate the field of ecology.Nature ecology & evolution · 2025Review
- Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Generation of synthetic TSPO PET maps from structural MRI images.Frontiers in neuroinformatics · 2025Article
- Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging.Frontiers in human neuroscience · 2025Article
- Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.Scientific data · 2024Article
- Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.ArXiv · 2024Article
- Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review.Journal of clinical medicine · 2024Review
- Article
- Paradigm shift required for translational research on the brain.Experimental & molecular medicine · 2024Review
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
Abstract
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.
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.