Evidence map›Paper›PMID 32010509›Full record

ArticleBiomedical optics express2020

Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning.

Mikko J Huttunen, Radu Hristu, Adrian Dumitru, Iustin Floroiu, Mariana Costache, Stefan G Stanciu

Open access · goldAbstract read
In one paragraph

Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
4.5field-weighted citation impact, top 6% 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

12 citing papers in PubMed, 28 citations in OpenAlex.

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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

6 authors at 3 institutions in 4 countries.

Mikko J HuttunenPhotonics Laboratory, Physics Unit, Tampere University, Tampere, Finland.
Radu HristuCenter for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania.ORCID https://orcid.org/0000-0001-8051-8253
Adrian DumitruDepartment of Pathology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.
Iustin FloroiuCenter for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania.
Mariana CostacheDepartment of Pathology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.
Stefan G StanciuCenter for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania.ORCID https://orcid.org/0000-0002-1676-3040
Universitatea Națională de Știință și Tehnologie Politehnica București · ROCarol Davila University of Medicine and Pharmacy · ROTampere University · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard for the diagnostics of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both

Identifiers

PMID32010509
PMCPMC6968761
OpenAlexW2995356951

What OpenQuestion holds

Textmetadata
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.