Evidence map›Paper›PMID 37749108›Full record

ArticleNature communications2023

Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy.

Matthew T Martell, Nathaniel J M Haven, Brendyn D Cikaluk, Brendon S Restall, Ewan A McAlister, Rohan Mittal, Benjamin A Adam, Nadia Giannakopoulos, Lashan Peiris, Sveta Silverman and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
6.8field-weighted citation impact, top 2% of its field
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

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.

  1. Pooled it
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  8. Quantitative Stain Mapping in X-Ray Virtual Histology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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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

13 authors at 1 institution in 1 country.

Matthew T Martell *Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.ORCID http://orcid.org/0000-0001-9927-894X
Nathaniel J M Haven *Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.ORCID http://orcid.org/0000-0002-1090-5503
Brendyn D CikalukDepartment of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
Brendon S RestallDepartment of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
Ewan A McAlisterDepartment of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
Rohan MittalDepartment of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.
Benjamin A AdamDepartment of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.ORCID http://orcid.org/0000-0003-1908-1739
Nadia GiannakopoulosDepartment of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.ORCID http://orcid.org/0000-0001-7543-088X
Lashan PeirisDepartment of Surgery, University of Alberta, 8440 - 112 Street, Edmonton, AB, T6G 2B7, Canada.
Sveta SilvermanDepartment of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.
Jean DeschenesDepartment of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.
Xingyu LiDepartment of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
Roger J ZempDepartment of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada. rzemp@ualberta.ca.ORCID http://orcid.org/0000-0001-6802-5624
University of Alberta · CA

Funding

CIHR PS 168936
6 · The paper itself

Abstract

The goal of oncologic surgeries is complete tumor resection, yet positive margins are frequently found postoperatively using gold standard H&E-stained histology methods. Frozen section analysis is sometimes performed for rapid intraoperative margin evaluation, albeit with known inaccuracies. Here, we introduce a label-free histological imaging method based on an ultraviolet photoacoustic remote sensing and scattering microscope, combined with unsupervised deep learning using a cycle-consistent generative adversarial network for realistic virtual staining. Unstained tissues are scanned at rates of up to 7 mins/cm

Indexed as

Deep LearningMicroscopyColoring AgentsHumansMaleRemote Sensing TechnologySpectrum AnalysisColoring Agents

Identifiers

PMID37749108
PMCPMC10519961
OpenAlexW4387015870

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.