ArticleJournal of imaging informatics in medicine2026
Enhanced Vertebra-GNN: A Graph-Based Deep Learning Framework for Anatomically Informed Spinal Disease Classification.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- DeepInsight-Net: a CBAM-enhanced ResNet50 framework with focal loss for robust cervical cancer classification on multi-center datasets.Frontiers in medicine · 2026Article
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate classification of spinal pathologies from radiographic images is essential for timely diagnosis and effective treatment planning. In this study, we propose an end-to-end deep learning framework that integrates convolutional neural networks (CNNs) with graph neural networks (GNNs) to model both the appearance and anatomical relationships of vertebrae from lateral spine X-rays. The proposed Enhanced Vertebra-GNN utilizes CNN-extracted regional features from segmented vertebral regions-of-interest and encodes inter-vertebral dependencies using a graph representation of the spine. Three variants of the GNN architecture-GCN, GAT, and Graph Transformer-were explored to optimize relational feature learning. Experimental results demonstrate the superiority of our model over conventional CNN baselines, with substantial gains in F
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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.