A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Azam, Kazi Sultana Farhana; Ryabchykov, Oleg; Bocklitz, Thomas

doi:10.3390/molecules27217448

Cited by 11 publications

(2 citation statements)

References 87 publications

(97 reference statements)

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“…Based on the single spectroscopic method, it is possible to build classification models to be used for the prediction of the cell subtype 51–56 . With TRAM, there is a unique possibility to combine data collected from various spectroscopic methods and perform data fusion analysis to improve the performance of the models and extend their applicability to more complex problems, including the identification of populations of cells in highly heterogeneous neoplasms, such as leukemia 57,58 .…”

Section: Resultsmentioning

confidence: 99%

Tandem RAman Microscopy (TRAM): integration of spontaneous and coherent Raman scattering offering data fusion analysis to improve optical biosensing

Brzozowski,

Pieczara,

Nowakowska

et al. 2024

Preprint

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For the first time, we provide Tandem RAman Microscopy (TRAM), a cutting-edge multimodal microscope that integrates the methods of Stimulated Raman Scattering (SRS), Coherent anti-Stokes Raman Scattering (CARS), and spontaneous (resonance) Raman Scattering ((R)RS). The device facilitates sequential RS imaging to collect full spectra from every sample location and rapid SRS-CARS scanning at specific wavelengths, offering a reliable and efficient analytical tool. The fingerprint spectral region can be included in the spectral imaging capabilities of CARS and SRS. Additionally, the flexibility of measuring conditions is improved by offering resonance-enhanced RS measurements using a continuous wave (CW) laser with two scanning mode options (high-speed galvo-scanners and a piezo-driven precision translation stage). We analyze the in vitro model of nonadherent leukocytes to illustrate the capabilities of this unique system, emphasizing the benefits of measuring the same sample with three different techniques without having to transfer it between microscopes. Additionally, polystyrene was investigated as a non-biological material. The advantages of each Raman technique are utilized when (R)RS, SRS, and CARS are combined into a single device. This paves the way for dependable chemical characterization in a wide range of scientific and industrial fields.

show abstract

Section: Resultsmentioning

confidence: 99%

Tandem RAman Microscopy (TRAM): integration of spontaneous and coherent Raman scattering offering data fusion analysis to improve optical biosensing

Brzozowski,

Pieczara,

Nowakowska

et al. 2024

Preprint

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show abstract

“…Data fusion is the process of gathering data from multiple sources to produce information that is more accuracy and meaningful compared to any individual data source [43]. This concept is not new for the nonmedical community as seen with social media, businesses, and autonomous driving applications.…”

Section: Data Fusionmentioning

confidence: 99%

Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients

Tran

Kretsch

LaValley

et al. 2023

Current Opinion in Infectious Diseases

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Purpose of review Immunocompromised patients are at high risk for infection. During the coronavirus disease (COVID-19) pandemic, immunocompromised patients exhibited increased odds of intensive care unit admission and death. Early pathogen identification is essential to mitigating infection related risk in immunocompromised patients. Artificial intelligence (AI) and machine learning (ML) have tremendous appeal to address unmet diagnostic needs. These AI/ML tools often rely on the wealth of data found in healthcare to enhance our ability to identify clinically significant patterns of disease. To this end, our review provides an overview of the current AI/ML landscape as it applies to infectious disease testing with emphasis on immunocompromised patients. Recent findings Examples include AI/ML for predicting sepsis in high risk burn patients. Likewise, ML is utilized to analyze complex host-response proteomic data to predict respiratory infections including COVID-19. These same approaches have also been applied for pathogen identification of bacteria, viruses, and hard to detect fungal microbes. Future uses of AI/ML may include integration of predictive analytics in point-of-care (POC) testing and data fusion applications. Summary Immunocompromised patients are at high risk for infections. AI/ML is transforming infectious disease testing and has great potential to address challenges encountered in the immune compromised population.

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Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow

Ganjizadeh,

Zawada,

Langer

et al. 2024

J Digit Imaging. Inform. med.

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A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Cited by 11 publications

References 87 publications

Tandem RAman Microscopy (TRAM): integration of spontaneous and coherent Raman scattering offering data fusion analysis to improve optical biosensing

Tandem RAman Microscopy (TRAM): integration of spontaneous and coherent Raman scattering offering data fusion analysis to improve optical biosensing

Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients

Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow

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