biostudies-literatureRho JChungnam National Universitye0280438https://www.ebi.ac.uk/biostudies/studies/S-EPMC9894403biostudies-literatureUnknown18(2)Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10-15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods and indices, radiography and ultrasound are the gold standards in the diagnosis of feline HCM. However, only 75% accuracy has been achieved using radiography alone. Therefore, we trained five residual architectures (ResNet50V2, ResNet152, InceptionResNetV2, MobileNetV2, and Xception) using 231 ventrodorsal radiographic images of cats (143 HCM and 88 normal) and investigated the optimal architecture for diagnosing feline HCM through radiography. To ensure the generalizability of the data, the x-ray images were obtained from 5 independent instituPloS oneDeep learning-based diagnosis of feline hypertrophic cardiomyopathy.PMC98944032020-1438-01Song KHKwon HJRho JShin SMJhang KNa KLee GShin HSon HYfalseDeep learning-based diagnosis of feline hypertrophic cardiomyopathy.Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10-15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods and indices, radiography and ultrasound are the gold standards in the diagnosis of feline HCM. However, only 75% accuracy has been achieved using radiography alone. Therefore, we trained five residual architectures (ResNet50V2, ResNet152, InceptionResNetV2, MobileNetV2, and Xception) using 231 ventrodorsal radiographic images of cats (143 HCM and 88 normal) and investigated the optimal architecture for diagnosing feline HCM through radiography. To ensure the generalizability of the data, the x-ray images were obtained from 5 independent institu2023-01-01T00:00:00Z20232025-04-26T18:12:26.627Z2025-04-06T15:43:33.918ZS-EPMC98944033673031910.1371/journal.pone.0280438