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# Convolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry

Scientific Reports

Nature Publishing Group UK

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### Abstract

Retinal oximetry is a non-invasive technique to investigate the hemodynamics, vasculature and health of the eye. Current techniques for retinal oximetry have been plagued by quantitatively inconsistent measurements and this has greatly limited their adoption in clinical environments. To become clinically relevant oximetry measurements must become reliable and reproducible across studies and locations. To this end, we have developed a convolutional neural network algorithm for multi-wavelength oximetry, showing a greatly improved calculation performance in comparison to previously reported techniques. The algorithm is calibration free, performs sensing of the four main hemoglobin conformations with no prior knowledge of their characteristic absorption spectra and, due to the convolution-based calculation, is invariable to spectral shifting. We show, herein, the dramatic performance improvements in using this algorithm to deduce effective oxygenation (SO 2), as well as the added functionality to accurately measure fractional oxygenation ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{SO}}}_{{\bf{2}}}^{{\boldsymbol{f}}{\boldsymbol{r}}}$$\end{document} ). Furthermore, this report compares, for the first time, the relative performance of several previously reported multi-wavelength oximetry algorithms in the face of controlled spectral variations. The improved ability of the algorithm to accurately and independently measure hemoglobin concentrations offers a high potential tool for disease diagnosis and monitoring when applied to retinal spectroscopy.

### Most cited references70

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### Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

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### Diabetic Retinopathy: Vascular and Inflammatory Disease

(2015)
Diabetic retinopathy (DR) is the leading cause of visual impairment in the working-age population of the Western world. The pathogenesis of DR is complex and several vascular, inflammatory, and neuronal mechanisms are involved. Inflammation mediates structural and molecular alterations associated with DR. However, the molecular mechanisms underlying the inflammatory pathways associated with DR are not completely characterized. Previous studies indicate that tissue hypoxia and dysregulation of immune responses associated with diabetes mellitus can induce increased expression of numerous vitreous mediators responsible for DR development. Thus, analysis of vitreous humor obtained from diabetic patients has made it possible to identify some of the mediators (cytokines, chemokines, and other factors) responsible for DR pathogenesis. Further studies are needed to better understand the relationship between inflammation and DR. Herein the main vitreous-related factors triggering the occurrence of retinal complication in diabetes are highlighted.
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### Author and article information

###### Contributors
dccote@cervo.ulaval.ca
###### Journal
Sci Rep
Sci Rep
Scientific Reports
Nature Publishing Group UK (London )
2045-2322
6 August 2019
6 August 2019
2019
: 9
###### Affiliations
[1 ]ISNI 0000 0004 1936 8390, GRID grid.23856.3a, Université Laval, CERVO Brain Research Center, Neuroscience, Quebec City, ; Québec, G1V 0A6 Canada
[2 ]ISNI 0000 0004 1936 8390, GRID grid.23856.3a, Université Laval, Center for optics, photonics and lasers (COPL), Physics Engineering, Quebec City, ; Québec, G1V 0A6 Canada
[3 ]ISNI 0000 0004 1936 8390, GRID grid.23856.3a, Université Laval, Department of software engineering and informatics, Quebec city, ; Québec, G1V 0A6 Canada
[4 ]Zilia inc., Quebec City, Québec, G1V 0A6 Canada
[5 ]GRID grid.17089.37, University of Alberta, Chemical and Materials Engineering, ; Edmonton, Alberta T6G 1H9 Canada
###### Article
47621
10.1038/s41598-019-47621-7
6684811
31388136

###### Categories
Article