ArticleJournal of veterinary internal medicine
Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation.
Article in Journal of veterinary internal medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Pre-anaesthetic risk assessment and management of dogs with myxomatous mitral valve disease: a spectrum of care narrative review.The Journal of small animal practice · 2026Review
- Lung ultrasound interpretation using deep learning for the detection of B-lines in dogs.Journal of veterinary internal medicine · 2026Article
- Scoring of swine lung images: a comparison between a computer vision system and human evaluators.Veterinary research · 2025Article
- Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation.Journal of veterinary internal medicineArticle
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Authors and funding
19 authors at 12 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) could improve accuracy and reproducibility of echocardiographic measurements in dogs. HYPOTHESIS: A neural network can be trained to measure echocardiographic left ventricular (LV) linear dimensions in dogs. ANIMALS: Training dataset: 1398 frames from 461 canine echocardiograms from a single specialist center. VALIDATION: 50 additional echocardiograms from the same center.
methodsTraining dataset: a right parasternal 4-chamber long axis frame from each study, labeled by 1 of 18 echocardiographers, marking anterior and posterior points of the septum and free wall. VALIDATION DATASET: End-diastolic and end-systolic frames from 50 studies, annotated twice (blindly) by 13 experts, producing 26 measurements of each site from each frame. The neural network also made these measurements. We quantified its accuracy as the deviation from the expert consensus, using the individual-expert deviation from consensus as context for acceptable variation. The deviation of the AI measurement away from the expert consensus was assessed on each individual frame and compared with the root-mean-square-variation of the individual expert opinions away from that consensus.
resultsFor the septum in end-diastole, individual expert opinions deviated by 0.12 cm from the consensus, while the AI deviated by 0.11 cm (P = .61). For LVD, the corresponding values were 0.20 cm for experts and 0.13 cm for AI (P = .65); for the free wall, experts 0.20 cm, AI 0.13 cm (P < .01). In end-systole, there were no differences between individual expert and AI performances. CONCLUSIONS AND CLINICAL IMPORTANCE: An artificial intelligence network can be trained to adequately measure linear LV dimensions, with performance indistinguishable from that of experts.
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