Evidence map›Paper›PMID 42378233›Full record

ArticlePloS one2026

Validation of deep learning enabled software MetronMind to measure vertebral heart size and vertebral left atrial size in dogs.

Katharine Tess Sykes, Sonya G Gordon, John J Craig, Sonya Wesselowski, Alice H Watson

Abstract readValidation Study
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Katharine Tess SykesCollege of Veterinary Medicine and Biomedical Sciences, Department of Small Animal Clinical Sciences, Texas A&M University, College Station, Texas, United States of America.
Sonya G GordonCollege of Veterinary Medicine and Biomedical Sciences, Department of Small Animal Clinical Sciences, Texas A&M University, College Station, Texas, United States of America.ORCID https://orcid.org/0000-0001-7733-7662
John J CraigMetronMind, Paso Robles, California, United States of America.
Sonya WesselowskiCollege of Veterinary Medicine and Biomedical Sciences, Department of Small Animal Clinical Sciences, Texas A&M University, College Station, Texas, United States of America.
Alice H WatsonIndependent Academic, United Kingdom.ORCID https://orcid.org/0009-0001-1048-6521

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVertebral heart size (VHS) and vertebral left atrial size (VLAS) are objective radiographic measurements of heart and left atrial size, respectively and are associated with inter- and intraobserver variability when measured by humans. Artificial intelligence (AI) tools to determine VHS and VLAS have been developed and may reduce variability and save time.

objectivesTo compare two manual methods for measuring VHS and VLAS on right and left lateral canine thoracic radiographs and to compare measurements of VHS and VLAS made by deep learning-enabled program, MetronMind, with those made by a trained observer on right and left lateral radiographs. ANIMALS: Client-owned dogs (n = 1058) including 80 breeds with a variety of heart sizes, thoracic conformations and radiographic quality.

methodsRetrospective, single-center, method-comparison study. Pearson's correlation, Bland-Altman plots and Passing-Bablok regression were used to assess agreement.

resultsCorrelation between traditional and modified manual measurements for VHS and VLAS was strong (r = 0.994 and r = 0.974, respectively), with minimal bias (-0.10 and 0.04 vertebrae, respectively) indicating that the modified methods closely approximate traditional measurements obtained from right lateral views. MetronMind measurements of VHS and VLAS from right lateral radiographs also correlated well with the human observer's modified measurements (r = 0.947 and r = 0.811 respectively), showing small mean biases (0.08 and 0.07 vertebrae, respectively). Correlation between left and right lateral radiographic measurements of VHS (0.87 and 0.91) was higher than for VLAS (0.73 and 0.64) and bias was larger for VHS (0.26 and 0.31 vertebrae) than for VLAS (-0.13 and -0.10 vertebrae) for humans and MetronMind, respectively.

Indexed as

Deep LearningHeartHeart AtriaSoftwareAnimalsDogsFemaleObserver VariationOrgan SizeRetrospective Studies

Identifiers

PMID42378233
PMCPMC13318042

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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.