Optical imaging of fluorescent carbon biomarkers using artificial neural networks

• Published in: Journal of biomedical optics Vol. 19; no. 11; p. 117007
• Main Authors: Dolenko, Tatiana A, Burikov, Sergey A, Vervald, Alexey M, Vlasov, Igor I, Dolenko, Sergey A, Laptinskiy, Kirill A, Rosenholm, Jessica M, Shenderova, Olga A
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.11.2014
• Subjects: Algorithms; Animals; Biomarkers; Biomarkers - chemistry; Carbon; Carbon - chemistry; Chickens; Data compression; Egg White - chemistry; Fluorescence; Inverse problems; Models, Chemical; Nanoparticles; Nanoparticles - chemistry; Nanostructure; Neural networks; Neural Networks (Computer); Optical Imaging - methods; Spectrometry, Fluorescence; autofluorescence; carbon nanoparticles; optical imaging; neural networks
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.19.11.117007

The principle possibility of extraction of fluorescence of nanoparticles in the presence of background autofluorescence of a biological environment using neural network algorithms is demonstrated. It is shown that the methods used allow detection of carbon nanoparticles fluorescence against the background of the autofluorescence of egg white with a sufficiently low concentration detection threshold (not more than 2  μg/ml for carbon dots and 3  μg/ml for nanodiamonds). It was also shown that the use of the input data compression can further improve the accuracy of solving the inverse problem by 1.5 times.