Evidence map›Paper›PMID 41584227›Full record

ArticleFrontiers in artificial intelligence2025

Detection of protein-losing enteropathy (PLE) ultrasonographic imaging features in dogs using deep learning neural networks.

Anne-Kathrin Reichert, Kariem Ali, Amna Asif, Romy M Heilmann

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2025. 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

4 authors.

Anne-Kathrin ReichertDepartment for Small Animals, College of Veterinary Medicine, University of Leipzig, Leipzig, SN, Germany.
Kariem AliDepartment of Computer Science and Software Engineering, Lancaster University Leipzig, Leipzig, SN, Germany.
Amna AsifDepartment of Computer Science and Software Engineering, Lancaster University Leipzig, Leipzig, SN, Germany.
Romy M HeilmannDepartment for Small Animals, College of Veterinary Medicine, University of Leipzig, Leipzig, SN, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI)-based models and algorithms may aid in achieving overall more efficient and accurate diagnostics in various medical specialties. Such AI-based tools could be integrated and potentially offer advantages over currently used diagnostic and monitoring algorithms, enabling the pursue of more individualized treatment options with potentially improved patient outcomes in the future. However, very few studies exploring the potential of AI-based tools have been reported in veterinary medicine. Diagnosis and subclassification of chronic inflammatory enteropathy (CIE) and protein-losing enteropathy (PLE), requiring an integrated approach including several diagnostic modalities, remains a challenge in clinical canine gastroenterology and might benefit from AI-based tools. Thus, we aimed to use AI-based

Indexed as

artificial intelligencecaninechronic inflammatory enteropathydeep learningdiagnosismachine learningmodelResNet

Identifiers

PMID41584227
PMCPMC12823995

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.