Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation

Abstract

Background: Artificial 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. Methods: Training 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. Results: For 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