Evidence map›Paper›PMID 42270117›Full record

ArticleEuropean respiratory review : an official journal of the European Respiratory Society2026

Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.

Eva Kuhar, Junsoo Park, Forough Jahandideh, Majid Komeili, Ahmed Sadeknury, Navneet Kang, Prarthna Karunamurthy, Mohammad Reza Zarei, Amir Ebrahimi, Sean E Gill and 6 more

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in European respiratory review : an official journal of the European Respiratory Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Eva KuharBlueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Junsoo ParkDepartment of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.
Forough JahandidehBlueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Majid KomeiliSchool of Computer Science, Carleton University, Ottawa, ON, Canada.
Ahmed SadeknuryBlueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Navneet KangBlueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Prarthna KarunamurthyBlueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.ORCID https://orcid.org/0009-0002-4686-1108
Mohammad Reza ZareiSchool of Computer Science, Carleton University, Ottawa, ON, Canada.
Amir EbrahimiSchool of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada.
Sean E GillDivision of Respirology, Department of Medicine, Western University, London, ON, Canada.
Patricia C LiawDepartment of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
Dean A FergussonBlueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Duncan J StewartDepartment of Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Arvind MerDepartment of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, Canada.ORCID https://orcid.org/0000-0001-8510-7963
Manoj M LaluBlueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada mlalu@toh.ca.ORCID https://orcid.org/0000-0002-0322-382X
LungInsight Collaboration

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Histological analysis is a cornerstone of pre-clinical respiratory disease research. It enables assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows. To date, its use in pre-clinical respiratory models has not been synthesised.We conducted a scoping review following the Joanna Briggs Institute guidelines. We searched MEDLINE and Embase (inception - January 2025) for pre-clinical studies using AI to analyse histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate.Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Haematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Pre-processing methods (

Indexed as

Artificial IntelligenceHistological TechniquesImage Interpretation, Computer-AssistedLungAnimalsDeep LearningDisease Models, AnimalHumansIntelligent SystemsPredictive Value of Tests

Identifiers

PMID42270117
PMCPMC13250683

What OpenQuestion holds

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LicenceCC BY-NC
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.