Next Article in Journal
Organic/Inorganic Species Synergistically Supported Unprecedented Vanadomolybdates
Previous Article in Journal
Composite Hydrogel Microspheres Encapsulating Hollow Mesoporous Imprinted Nanoparticles for Selective Capture and Separation of 2′-Deoxyadenosine
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Molecules
Volume 27
Issue 21
10.3390/molecules27217448
molecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

by
Kazi Sultana Farhana Azam
1,2,
Oleg Ryabchykov
1,2 and
Thomas Bocklitz
1,2,*
1
Leibniz Institute of Photonic Technology, Member of Leibniz-Research Alliance “Health Technologies”, Albert-Einstein-Straße 9, 07745 Jena, Germany
2
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
*
Author to whom correspondence should be addressed.
Molecules 2022, 27(21), 7448; https://doi.org/10.3390/molecules27217448
Submission received: 9 August 2022 / Revised: 19 September 2022 / Accepted: 21 October 2022 / Published: 2 November 2022
(This article belongs to the Section Analytical Chemistry)
Browse Figures
Versions Notes

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.
Keywords: data fusion; ultrasonography; single photon emission computed tomography; positron emission tomography; magnetic resonance imaging; computed tomography; Raman spectroscopy; MALDI imaging; mammography; fluorescence lifetime imaging microscopy; deep learning; machine learning data fusion; ultrasonography; single photon emission computed tomography; positron emission tomography; magnetic resonance imaging; computed tomography; Raman spectroscopy; MALDI imaging; mammography; fluorescence lifetime imaging microscopy; deep learning; machine learning

Share and Cite

MDPI and ACS Style

Azam, K.S.F.; Ryabchykov, O.; Bocklitz, T. A Review on Data Fusion of Multidimensional Medical and Biomedical Data. Molecules 2022, 27, 7448. https://doi.org/10.3390/molecules27217448

AMA Style

Azam KSF, Ryabchykov O, Bocklitz T. A Review on Data Fusion of Multidimensional Medical and Biomedical Data. Molecules. 2022; 27(21):7448. https://doi.org/10.3390/molecules27217448

Chicago/Turabian Style

Azam, Kazi Sultana Farhana, Oleg Ryabchykov, and Thomas Bocklitz. 2022. "A Review on Data Fusion of Multidimensional Medical and Biomedical Data" Molecules 27, no. 21: 7448. https://doi.org/10.3390/molecules27217448

APA Style

Azam, K. S. F., Ryabchykov, O., & Bocklitz, T. (2022). A Review on Data Fusion of Multidimensional Medical and Biomedical Data. Molecules, 27(21), 7448. https://doi.org/10.3390/molecules27217448

Article Metrics

Back to TopTop