Evidence map›Paper›PMID 34797972›Full record

ArticleAnalytical chemistry2021

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy.

Conor C Horgan, Magnus Jensen, Anika Nagelkerke, Jean-Philippe St-Pierre, Tom Vercauteren, Molly M Stevens, Mads S Bergholt

Abstract read
In one paragraph

Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Review
  2. Longitudinal Morphomolecular Monitoring of Head and Neck Carcinogenesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Conor C HorganCentre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K.
Magnus JensenCentre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K.
Anika NagelkerkeGroningen Research Institute of Pharmacy, Pharmaceutical Analysis, University of Groningen, P.O. Box 196, XB20, Groningen 9700 AD, The Netherlands.
Jean-Philippe St-PierreDepartment of Chemical and Biological Engineering, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada.
Tom VercauterenSchool of Biomedical Engineering and Imaging Sciences, King's College London, London WC2R 2LS, U.K.
Molly M StevensDepartment of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, U.K.ORCID 0000-0002-7335-266X
Mads S BergholtCentre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K.ORCID 0000-0003-3986-8942

Funding

Wellcome Trust 098411/Z/12/ZWellcome Trust 203148/Z/16/ZWellcome Trust WT 203148/Z/16/Z
6 · The paper itself

Abstract

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.

Indexed as

Deep LearningSpectrum Analysis, RamanMolecular ImagingNeural Networks, ComputerSignal-To-Noise Ratio

Identifiers

PMID34797972
PMCPMC9286315

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.