Evidence map›Paper›PMID 40879571›Full record

ArticleInternational forum of allergy & rhinology2025

Classification and Segmentation of Mucus Morphology Using Deep Learning During Diagnostic Nasal Endoscopy.

Dipesh Gyawali, Jonathan Bidwell, Sejal Shyam Bhatia, Akio Fujiwara, Thomas Mundy, Kayla Baker, Elena Bartolone, Dhara Patel, Edward D McCoul

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International forum of allergy & rhinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Artificial Intelligence in Rhinology: A State-of-the-Art Review of Clinical Readiness and Implementation Pathways.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026
    Review
  2. 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

9 authors.

Dipesh GyawaliDepartment of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.ORCID 0009-0001-3734-6000
Jonathan BidwellDepartment of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
Sejal Shyam BhatiaDepartment of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
Akio FujiwaraDepartment of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
Thomas MundyOchsner Clinical School, University of Queensland, New Orleans, Louisiana, USA.
Kayla BakerOchsner Clinical School, University of Queensland, New Orleans, Louisiana, USA.
Elena BartoloneOchsner Clinical School, University of Queensland, New Orleans, Louisiana, USA.
Dhara PatelOchsner Clinical School, University of Queensland, New Orleans, Louisiana, USA.
Edward D McCoulDepartment of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.ORCID 0000-0003-1812-2105

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

key pointsA pretrained deep learning model can segment and classify mucus morphologies encountered during nasal endoscopy. Strong performance for different mucus morphologies supports the detection of sinonasal inflammation. This is the first study to objectively classify mucus morphology in nasal settings.

Indexed as

Deep LearningEndoscopyMucusNasal MucosaSinusitisHumansartificial intelligencedeep learningdiagnosismucusnasal endoscopysinusitis

Identifiers

PMID40879571
PMCPMC12581871

What OpenQuestion holds

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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.