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In this study, we developed a new deep learning-based radiographic index quantifying canine heart size using retrospective data. The proposed \u201cadjusted heart volume index\u201d (aHVI) was calculated as the total area of the heart multiplied by the heart\u2019s height and divided by the fourth thoracic vertebral body (T4) length from simple lateral X-rays. The algorithms consist of segmentation and measurements. For semantic segmentation, we used 1000 dogs\u2019 radiographic images taken between Jan 2018 and Aug 2020 at Seoul National University Veterinary Medicine Teaching Hospital. The tversky loss functions with multiple hyperparameters were used to capture the size-unbalanced regions of heart and T4. 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