ReviewSensors (Basel, Switzerland)2021
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Enhanced Vertebra-GNN: A Graph-Based Deep Learning Framework for Anatomically Informed Spinal Disease Classification.Journal of imaging informatics in medicine · 2026Article
- Article
- High-precision classification of WCE-based gastrointestinal abnormality using a fusion deep learning approach.Scientific reports · 2026Article
- Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.PLOS digital health · 2026Article
- Attention-based graph neural network framework for non-invasive CAP score prediction in fatty liver disease via body modeling.Physical and engineering sciences in medicine · 2026Article
- Multimodal spatiotemporal graph convolutional attention network for dynamic risk stratification and intervention strategy generation in rare disease rehabilitation nursing.Scientific reports · 2026Article
- Multimodal MRI to elucidate cross-organ coupling mechanisms of the heart-brain axis.Frontiers in cardiovascular medicine · 2026Review
- Volumetric spline-based Kolmogorov-Arnold architectures surpass CNNs, vision transformers, and graph networks for Parkinson's disease detection.Communications medicine · 2025Article
- Development of a deep-learning algorithm for etiological classification of subarachnoid hemorrhage using non-contrast CT scans.European radiology · 2025Article
- The Enhance-Fuse-Align Principle: A New Architectural Blueprint for Robust Object Detection, with Application to X-Ray Security.Sensors (Basel, Switzerland) · 2025Article
- Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson's Disease Detection.Journal of imaging · 2025Article
- Application of improved graph convolutional network for cortical surface parcellation.Scientific reports · 2025Article
- Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine.Journal of imaging informatics in medicine · 2025Article
- Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances.Frontiers in neuroscience · 2025Review
- Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data.Frontiers in computational neuroscience · 2025Article
- Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph.Frontiers in oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.
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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.