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TitleConvolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry.
Publication TypeJournal Article
Year of Publication2019
AuthorsDePaoli, Damon T., Prudencio Tossou, Martin Parent, Dominic Sauvageau, and Daniel C. Côté
JournalSci Rep
Volume9
Issue1
Pagination11387
Date Published2019 Aug 06
ISSN2045-2322
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), as well as the added functionality to accurately measure fractional oxygenation ([Formula: see text]). 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.

DOI10.1038/s41598-019-47621-7
Alternate JournalSci Rep
PubMed ID31388136
PubMed Central IDPMC6684811