ArticleScientific reports2020
Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 4 of them syntheses that pooled it.
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Who cites it
80 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Accuracy of AI-Based Algorithms in Pulmonary Embolism Detection on Computed Tomographic Pulmonary Angiography: An Updated Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography.Frontiers in radiology · 2024Pooled it
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis.Scientific reports · 2021Pooled it
- FUO-PETMamba: a pet maximum-intensity-projection-based artificial intelligence framework for aetiological classification of fever of unknown origin with multicentre validation.European journal of nuclear medicine and molecular imaging · 2026Article
- Artificial intelligence in computed tomography imaging for pulmonary embolism: a narrative review from computed tomography pulmonary angiography to non-contrast computed tomography.Quantitative imaging in medicine and surgery · 2026Review
- Visual prompt engineering for multimodal and irregularly sampled medical data.Communications medicine · 2026Article
- Hierarchical Multimodal Fusion of Multi-Sequence MRI and Clinical Metadata for the Classification of Rotator Cuff Tears.Journal of clinical medicine · 2026Article
- Multimodal algorithmic fusion model for physical education assessment: spatiotemporal comparison of inertial measurement units and traditional scales.BMC sports science, medicine & rehabilitation · 2026Article
- Article
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images.medRxiv : the preprint server for health sciences · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies.Journal of imaging informatics in medicine · 2026Article
- Deep learning in the early diagnosis of acute aortic dissection.Frontiers in cardiovascular medicine · 2026Review
- MAF-Net: Multimodal cross-attention-based fusion network for cardiovascular disease classification.PloS one · 2026Article
- Hierarchical spatio-temporal graph network for risk prediction.Scientific reports · 2025Article
- Simulation-assisted multimodal deep learning (Sim-MDL) fusion models for the evaluation of thermal barrier coatings using infrared thermography and Terahertz imaging.Scientific reports · 2025Article
- Artificial Intelligence in Medicine: Moving From "Prediction" to "Patient-Centric Decision Intelligence".Cureus · 2025Article
- Improvement of artificial intelligence-based computed tomography pulmonary angiography in identifying acute pulmonary embolism.Quantitative imaging in medicine and surgery · 2025Article
20 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
Recent advancements in deep learning have led to a resurgence of medical imaging and Electronic Medical Record (EMR) models for a variety of applications, including clinical decision support, automated workflow triage, clinical prediction and more. However, very few models have been developed to integrate both clinical and imaging data, despite that in routine practice clinicians rely on EMR to provide context in medical imaging interpretation. In this study, we developed and compared different multimodal fusion model architectures that are capable of utilizing both pixel data from volumetric Computed Tomography Pulmonary Angiography scans and clinical patient data from the EMR to automatically classify Pulmonary Embolism (PE) cases. The best performing multimodality model is a late fusion model that achieves an AUROC of 0.947 [95% CI: 0.946-0.948] on the entire held-out test set, outperforming imaging-only and EMR-only single modality models.
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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.