ReviewMolecules (Basel, Switzerland)2022
A Review on Data Fusion of Multidimensional Medical and Biomedical Data.
Review in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- [Advances in deep learning multimodal fusion for early diagnosis of knee osteoarthritis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study).International journal of computer assisted radiology and surgery · 2026Article
- Early fusion of laser and acoustic features for human orientation detection in non-line-of-sight environments.Scientific reports · 2026Article
- Article
- An interpretable multimodal ensemble assessment framework for Alzheimer's disease cognitive staging.Frontiers in neurology · 2026Article
- A Museum artifact classification model based on cross-modal attention fusion and generative data augmentation.Scientific reports · 2025Article
- Multimodal data fusion in neuroscience: promises, challenges and future directions.IEEE signal processing magazine · 2025Article
- Organ crosstalk and dysfunction in sepsis.Annals of intensive care · 2024Review
- Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.Journal of imaging informatics in medicine · 2024Article
- Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology.Radiation oncology (London, England) · 2024Article
- Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow.Journal of imaging informatics in medicine · 2024Article
- Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.
Indexed as
Identifiers
What Socratic 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 Socratic graph.