ArticleScientific reports2019
Convolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
11 citing papers in PubMed.
- Enhancing the detection of LTP through lyophilized protein samples and NIR spectroscopy with explainable deep learning.Scientific reports · 2026Article
- Retinal arteriovenous oxygen saturation difference is inversely associated with cardiac output measured by echocardiography: a non-invasive pilot study.Scientific reports · 2026Observational
- Evaluation of macular retinal oximetry across different levels of diabetic retinopathy: a cross sectional study.BMC ophthalmology · 2025Article
- Test-retest repeatability in macular retinal oximetry.Clinical & experimental optometry · 2024Article
- Variations in Retinal Oxygen Saturation in a Diverse Healthy Population.Clinical optometry · 2024Article
- Targeted spectroscopy in the eye fundus.Journal of biomedical optics · 2023Article
- Sensitivity of visible range multi-wavelength algorithms for retinal tissue oximetry to acquisition parameters.Biomedical optics express · 2023Article
- Novel prediction models for hyperketonemia using bovine milk Fourier-transform infrared spectroscopy.Preventive veterinary medicine · 2023Article
- Prediction Model of International Trade Risk Based on Stochastic Time-Series Neural Network.Computational intelligence and neuroscience · 2022Article
- Advances in Retinal Oximetry.Translational vision science & technology · 2021Review
- Rise of Raman spectroscopy in neurosurgery: a review.Journal of biomedical optics · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.