Evidence map›Paper›PMID 40119072›Full record

ArticleScientific reports2025

Bulldogs stenosis degree classification using synthetic images created by generative artificial intelligence.

Gustavo da Silva Andrade, Gabriel Toshio Hirokawa Higa, Jarbas Felipe da Silva Ribeiro, Joyce Katiuccia Medeiros Ramos Carvalho, Wesley Nunes Gonçalves, Marco Hiroshi Naka, Hemerson Pistori

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Gustavo da Silva AndradeUniversidade Federal de Mato Grosso do Sul, Campo Grande, Brazil. gustavo.s.andrade@ufms.br.
Gabriel Toshio Hirokawa HigaUniversidade Católica Dom Bosco, Campo Grande, Brazil.
Jarbas Felipe da Silva RibeiroUniversidade Católica Dom Bosco, Campo Grande, Brazil.
Joyce Katiuccia Medeiros Ramos CarvalhoUniversidade Católica Dom Bosco, Campo Grande, Brazil.
Wesley Nunes GonçalvesUniversidade Federal de Mato Grosso do Sul, Campo Grande, Brazil.
Marco Hiroshi NakaUniversidade Católica Dom Bosco, Campo Grande, Brazil.
Hemerson PistoriUniversidade Católica Dom Bosco, Campo Grande, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nasal stenosis in bulldogs significantly impacts their quality of life, making early diagnosis crucial for effective treatment. This study developed an automated deep learning model to classify the severity of nasal stenosis using 1020 images of bulldog nostrils, including both real and AI-generated samples. Five neural network architectures were tested across three experiments, with DenseNet201 achieving the highest median F-score of 54.04%. The model's performance was directly compared to trained human evaluators specializing in veterinary anatomy, achieving comparable levels of accuracy and reliability. These results demonstrate the potential of advanced neural networks to match human-level performance in diagnosis, paving the way for enhanced treatment planning and overall animal welfare.

Indexed as

Artificial IntelligenceDog DiseasesImage Processing, Computer-AssistedNasal ObstructionAnimalsConstriction, PathologicDeep LearningDogsGenerative Artificial IntelligenceHumansNeural Networks, ComputerAirway obstructionBrachycephalic dogsBrachycephalic obstructive airway syndromeComputer visionDeep learningStenotic nares

Identifiers

PMID40119072
PMCPMC11928557

What OpenQuestion holds

Textmetadata
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.