Key result
MetronMind deep learning software correlates strongly with human measurements of canine cardiac size.
Why the study?
Manual radiographic measurement of vertebral heart size and vertebral left atrial size in dogs has inter- and intraobserver variability, which artificial intelligence tools may reduce while saving time.
Does the deep learning-enabled software MetronMind provide accurate and comparable vertebral heart size (VHS) and vertebral left atrial size (VLAS) measurements compared to a trained human observer on canine thoracic radiographs?
Observational (n=1,058)
No
Does the deep learning-enabled software MetronMind provide accurate and comparable vertebral heart size (VHS) and vertebral left atrial size (VLAS) measurements compared to a trained human observer on canine thoracic radiographs?
Answers are grounded in this paper and cite their sources.
MetronMind deep learning software provides rapid, repeatable measurements of vertebral heart size and vertebral left atrial size on canine right lateral thoracic radiographs that closely agree with a trained human observer.
Background Vertebral 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. Objectives To 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. Methods Retrospective, single-center, method-comparison study. Pearson’s correlation, Bland-Altman plots and Passing-Bablok regression were used to assess agreement. Results Correlation 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.
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Sykes et al. (2026) conducted an observational in Canine cardiac assessment (structurally normal hearts, myxomatous mitral valve disease, dilated cardiomyopathy, and arrhythmogenic cardiomyopathy) (n=1,058). MetronMind deep learning-enabled software (version 4.4.3) vs. Manual radiographic measurement (traditional and modified methods) by a trained human observer was evaluated on Agreement and correlation of vertebral heart size (VHS) and vertebral left atrial size (VLAS) on right lateral radiographs between MetronMind AI and modified human observer measurements (r = 0.947 (VHS), r = 0.811 (VLAS), 95% CI 0.940-0.953). MetronMind deep learning software correlated strongly with human measurements on right lateral canine radiographs for VHS and VLAS.
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