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Cardiomegaly is the main imaging finding for canine heart diseases. There are many advances in the field of medical diagnosing based on imaging with deep learning for human being. However there are also increasing realization of the potential of using deep learning in veterinary medicine. We reported a clinically applicable assisted platform for diagnosing the canine cardiomegaly with deep learning. VHS (vertebral heart score) is a measuring method used for the heart size of a dog. The concrete value of VHS is calculated with the relative position of 16 key points detected by the system, and this result is then combined with VHS reference range of all dog breeds to assist in the evaluation of the canine cardiomegaly. We adopted HRNet (high resolution network) to detect 16 key points (12 and four key points located on vertebra and heart respectively) in 2274 lateral X-ray images (training and validation datasets) of dogs, the model was then used to detect the key points in external testing dataset (396 images), the AP (average performance) for key point detection reach 86.4 %. Then we applied an additional post processing procedure to correct the output of HRNets so that the AP reaches 90.9 %. This result signifies that this system can effectively assist the evaluation of canine cardiomegaly in a real clinical scenario. [ABSTRACT FROM AUTHOR]