Kidney volume is associated with renal function and the severity of renal diseases, thus accurate assessment of the kidney is important. Although the voxel count method is reported to be more accurate than several methods, its laborious and time-consuming process is considered as a main limitation. In need of a new technology that is fast and as accurate as the manual voxel count method, the aim of this study was to develop the first deep learning model for automatic kidney detection and volume estimation from computed tomography (CT) images of dogs. A total of 182,974 image slices from 386 CT scans of 211 dogs were used to develop this deep learning model. Owing to the variance of kidney size and location in dogs compared to humans, several processing methods and an architecture based on UNEt Transformers which is known to show promising results for various medical image segmentation tasks including this study. Combined loss function and data augmentation were applied to elevate the performance of the model. The Dice similarity coefficient (DSC) which shows the similarity between manual segmentation and automated segmentation by deep-learning model was 0.915 ± 0.054 (mean ± SD) with post-processing. Kidney volume agreement analysis assessing the similarity between the kidney volume estimated by manual voxel count method and the deep-learning model was r = 0.960 (p < 0.001), 0.95 from Lin's concordance correlation coefficient (CCC), and 0.975 from the intraclass correlation coefficient (ICC). Kidney volume was positively correlated with body weight (BW), and insignificantly correlated with body conditions score (BCS), age, and sex. The correlations between BW, BCS, and kidney volume were as follows: kidney volume = 3.701 × BW + 11.962 (R² = 0.74, p < 0.001) and kidney volume = 19.823 × BW/BCS index + 10.705 (R² = 0.72, p < 0.001). The deep learning model developed in this study is useful for the automatic estimation of kidney volume. Furthermore, a reference range established in this study for CT-based normal kidney volume considering BW and BCS can be helpful in assessment of kidney in dogs.

Bacterial skin infections, particularly pyoderma and otitis externa, are widespread in dogs, primarily caused by Staphylococcus and Pseudomonas species. This study evaluates the prevalence and types of bacterial pathogens in affected dogs in South Korea using a meta-analytical approach. Following the PRISMA guidelines, five electronic databases were searched for relevant studies published between 1990 and 2024. Three researchers independently performed data extraction and quality assessment. A subgroup analysis explored the variability in pathogen prevalence across studies based on bacterial genus, bacterial species, publication year, sampling year, sampling location, infection type, diagnostic method, and sample size. Publication bias was evaluated using funnel plots and Egger’s regression test, with all analyses conducted using the R program. Of the 944 articles, 29 met the eligibility criteria. The pooled bacterial prevalence among infected dogs was 99.95% (95%CI: 99.85–100). Staphylococcus was the most prevalent genus (95.93%), followed by Pseudomonas (48.43%), Enterococcus (20.32%), and Escherichia (17.63%). The most common species were Staphylococcus pseudintermedius (78.89%), Staphylococcus intermedius (71.43%), and Pseudomonas aeruginosa (46.13%). This study underscores the need for comprehensive treatment strategies targeting Gram-negative and Gram-positive bacteria, emphasizing further research on antimicrobial resistance patterns and treatment efficacy to enhance canine health outcomes in South Korea.

Nephrolithiasis is one of the most common urinary disorders in dogs. Although a majority of kidney calculi are non-obstructive and are likely to be asymptomatic, they can lead to parenchymal loss and obstruction as they progress. Thus, early diagnosis of kidney calculi is important for patient monitoring and better prognosis. However, detecting kidney calculi and monitoring changes in the sizes of the calculi from computed tomography (CT) images is time-consuming for clinicians. This study, in a first of its kind, aims to develop a deep learning model for automatic kidney calculi detection using pre-contrast CT images of dogs. A total of 34,655 transverseimage slices obtained from 76 dogs with kidney calculi were used to develop the deep learning model. Because of the differences in kidney location and calculi sizes in dogs compared to humans, several processing methods were used. The first stage of the models, based on the Attention U-Net (AttUNet), was designed to detect the kidney for the coarse feature map. Five different models–AttUNet, UTNet, TransUNet, SwinUNet, and RBCANet–were used in the second stage to detect the calculi in the kidneys, and the performance of the models was evaluated. Compared with a previously developed model, all the models developed in this study yielded better dice similarity coefficients (DSCs) for the automatic segmentation of the kidney. To detect kidney calculi, RBCANet and SwinUNet yielded the best DSC, which was 0.74. In conclusion, the deep learning model developed in this study can be useful for the automated detection of kidney calculi.