ArticleiScience2023
Bimodal artificial intelligence using TabNet for differentiating spinal cord tumors-Integration of patient background information and images.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Transformer-based multimodal model for estimation of appendicular lean mass using incomplete chest radiographs and electronic health record.Journal of translational medicine · 2026Article
- Development and external validation of an interpretable deep learning model for early immune-related adverse events in hepatocellular carcinoma.Frontiers in immunology · 2026Article
- Article
- Development and validation of a multi-modal MRI-based deep learning framework for differentiation of intraspinal tumors (ISMF-Net).EClinicalMedicine · 2025Article
- Deep Learning-Based 30-Day Mortality Prediction in Critically Ill Bone and Bone Marrow Metastasis Patients: A Multicenter Retrospective Cohort Study.Current oncology (Toronto, Ont.) · 2025Article
- The Application of Artificial Intelligence in Spine Surgery: A Scoping Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025Article
- Bimodal machine learning model for unstable hips in infants: integration of radiographic images with automatically-generated clinical measurements.Scientific reports · 2024Article
- Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation.Frontiers in medicine · 2024Article
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We proposed a bimodal artificial intelligence that integrates patient information with images to diagnose spinal cord tumors. Our model combines TabNet, a state-of-the-art deep learning model for tabular data for patient information, and a convolutional neural network for images. As training data, we collected 259 spinal tumor patients (158 for schwannoma and 101 for meningioma). We compared the performance of the image-only unimodal model, table-only unimodal model, bimodal model using a gradient-boosting decision tree, and bimodal model using TabNet. Our proposed bimodal model using TabNet performed best (area under the receiver-operating characteristic curve [AUROC]: 0.91) in the training data and significantly outperformed the physicians' performance. In the external validation using 62 cases from the other two facilities, our bimodal model showed an AUROC of 0.92, proving the robustness of the model. The bimodal analysis using TabNet was effective for differentiating spinal tumors.
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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.