Evidence map›Paper›PMID 37671317›Full record

ArticlePhotoacoustics2023

Deep learning assisted classification of spectral photoacoustic imaging of carotid plaques.

Camilo Cano, Nastaran Mohammadian Rad, Amir Gholampour, Marc van Sambeek, Josien Pluim, Richard Lopata, Min Wu

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

Article in Photoacoustics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
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  3. Article
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  5. Article
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 at 1 institution in 1 country.

Camilo CanoDepartment of Biomedical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands.
Nastaran Mohammadian RadDepartment of Precision Medicine, Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands.
Amir GholampourDepartment of Biomedical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands.
Marc van SambeekDepartment of Vascular Surgery, Catharina Ziekenhuis Eindhoven, Michelangelolaan 2, State Two, the Netherlands.
Josien PluimDepartment of Biomedical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands.
Richard LopataDepartment of Biomedical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands.
Min WuDepartment of Biomedical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands.
Eindhoven University of Technology · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spectral photoacoustic imaging (sPAI) is an emerging modality that allows real-time, non-invasive, and radiation-free assessment of tissue, benefiting from their optical contrast. sPAI is ideal for morphology assessment in arterial plaques, where plaque composition provides relevant information on plaque progression and its vulnerability. However, since sPAI is affected by spectral coloring, general spectroscopy unmixing techniques cannot provide reliable identification of such complicated sample composition. In this study, we employ a convolutional neural network (CNN) for the classification of plaque composition using sPAI. For this study, nine carotid endarterectomy plaques were imaged and were then annotated and validated using multiple histological staining. Our results show that a CNN can effectively differentiate constituent regions within plaques without requiring fluence or spectra correction, with the potential to eventually support vulnerability assessment in plaques.

Indexed as

Carotid plaqueConvolutional neural networkSpectral photoacoustic imaging

Identifiers

PMID37671317
PMCPMC10475504
OpenAlexW4385879625

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.