Fintan J. McEvoy

This study evaluated three computed tomography (CT)‐based automatic fat segmentation methods and identified the optimal regression model for predicting whole‐body fat in rabbits. CT scans were performed on 21 postmortem companion rabbits to estimate fat percentage (Fat%‐CT) and fat volume (FV‐CT, mm ³ ). Two techniques were used to determine CT number (Hounsfield units [HU]) ranges for fat (HU Ranges 1 and 2). These were applied to four body regions using three voxel counting techniques (one‐, two‐, and three‐dimensional techniques) to quantify Fat%‐CT and FV‐CT. Data from whole‐body chemical carcass analysis (whole‐body Fat% and whole‐body fat in grams) were used as gold standard values. The relationship between the extracted CT data and carcass fat estimates was determined using a Spearman rank correlation. Simple linear regression and assumption tests were performed to identify the best model for predicting whole‐body Fat%, and whole‐body fat (g). There was a high correlation between measured fat estimated by CT and by chemical carcass analysis ( r _s > 0.7, p < 0.001) for all but one body region—counting combination. The best model predicting whole‐body Fat% used CT data from left kidney region, HU Range 2, and three‐dimensional counting technique. The optimal model predicting whole‐body fat (g) used CT data from the entire abdominal region, HU Range 1, and a one‐dimensional counting technique. The CT‐based models for estimating whole‐body fat in rabbits are feasible. Model optimization requires appropriate selection of the body region, CT number ranges, and counting techniques.

Canine hip dysplasia (CHD) screening relies on radiographic assessment, but traditional scoring methods often lack consistency due to inter-rater variability. This study presents an AI-driven system for automated measurement of the femoral head center to dorsal acetabular edge (FHC/DAE) distance, a key metric in CHD evaluation. Unlike most AI models that directly classify CHD severity using convolutional neural networks, this system provides an interpretable, measurement-based output to support a more transparent evaluation. The system combines a keypoint regression model for femoral head center localization with a U-Net-based segmentation model for acetabular edge delineation. It was trained on 7967 images for hip joint detection, 571 for keypoints, and 624 for acetabulum segmentation, all from ventrodorsal hip-extended radiographs. On a test set of 70 images, the keypoint model achieved high precision (Euclidean Distance = 0.055 mm; Mean Absolute Error = 0.0034 mm; Mean Squared Error = 2.52 × 10⁻⁵ mm²), while the segmentation model showed strong performance (Dice Score = 0.96; Intersection over Union = 0.92). Comparison with expert annotations demonstrated strong agreement (Intraclass Correlation Coefficients = 0.97 and 0.93; Weighted Kappa = 0.86 and 0.79; Standard Error of Measurement = 0.92 to 1.34 mm). By automating anatomical landmark detection, the system enhances standardization, reproducibility, and interpretability in CHD radiographic assessment. Its strong alignment with expert evaluations supports its integration into CHD screening workflows for more objective and efficient diagnosis and CHD scoring.

Canine hip dysplasia (CHD) screening relies on accurate positioning in the ventrodorsal hip extended (VDHE) view, as even mild pelvic rotation can affect CHD scoring and impact breeding decisions. This study aimed to assess the association between pelvic rotation and asymmetry in obturator foramina areas (AOFAs) and to develop a computer vision model for automated AOFA measurement. In the first part, 203 radiographs were analyzed to examine the relationship between pelvic rotation, assessed through asymmetry in iliac wing and obturator foramina widths (AOFWs), and AOFAs. A significant association was found between pelvic rotation and AOFA, with AOFW showing a stronger correlation (R² = 0.92, p < 0.01). AOFW rotation values were categorized into minimal (n = 71), moderate (n = 41), marked (n = 37), and extreme (n = 54) groups, corresponding to mean AOFA ± standard deviation values of 33.28 ± 27.25, 54.73 ± 27.98, 85.85 ± 41.31, and 160.68 ± 64.20 mm², respectively. ANOVA and post hoc testing confirmed significant differences in AOFA across these groups (p < 0.01). In part two, the dataset was expanded to 312 images to develop the automated AOFA model, with 80% allocated for training, 10% for validation, and 10% for testing. On the 32 test images, the model achieved high segmentation accuracy (Dice score = 0.96; Intersection over Union = 0.93), closely aligning with examiner measurements. Paired t-tests indicated no significant differences between the examiner and model’s outputs (p > 0.05), though the Bland–Altman analysis identified occasional discrepancies. The model demonstrated excellent reliability (ICC = 0.99) with a standard error of 17.18 mm². A threshold of 50.46 mm² enabled effective differentiation between acceptable and excessive pelvic rotation. With additional training data, further improvements in precision are expected, enhancing the model’s clinical utility.

Pyometra is a prevalent disease in intact bitches and the standard treatment is ovariohysterectomy (OHE). Published descriptions of normal sonographic findings after OHE are currently lacking. The aims of this prospective observational study were to describe and compare postoperative abdominal sonographic features for three timepoints following OHE in a group of dogs with pyometra and an uneventful recovery. A total of 22 dogs had sequential focused abdominal ultrasound examinations on days 1, 4-6, and 10-15 postsurgery. Recorded sonographic features for each examination time point and characteristics of the cervical stump and the mesovarium, size, and echogenicity of medial iliac lymph nodes (MILNs), presence of free peritoneal fluid, and pneumoperitoneum. The cervical stump appeared as a heterogenous area with a hypoechoic center surrounded by hyperechogenic tissue in all dogs. The cervical stump transverse-sectional area was larger on day 4-6 compared with day 1 and day 10-15 (P = .0009). Mesovarium ligature reactions were identified as heterogeneous and hyperechoic areas with central and/or edge shadowing in all dogs. The size and echogenicity of MILNs and the mesovarium reactions did not significantly differ among time points. Free peritoneal fluid was detected in 45%, 41%, and 9% and pneumoperitoneum in 95%, 82%, and 14% of dogs at sequential time points. Findings from this sample of dogs with an uneventful recovery following OHE due to pyometra can be used to assist veterinarians in interpreting postoperative abdominal ultrasonographic characteristics in future dogs treated surgically for pyometra.

The alteration in the shape of the femoral neck is an important radiographic sign for scoring canine hip dysplasia (CHD). Previous studies have reported that the femoral neck thickness (FNT) is greater in dogs with hip joint dysplasia, becoming progressively thicker with disease severity. The main objective of this work was to describe a femoral neck thickness index (FNTi) to quantify FNT and to study its association with the degree of CHD using the Fédération Cynologique Internationale (FCI) scheme. A total of 53 dogs (106 hips) were randomly selected for this study. Two examiners performed FNTi estimation to study intra- and inter-examiner reliability and agreement. The paired t-test, the Bland-Altman plots, and the intraclass correlation coefficient showed excellent agreement and reliability between the measurements of the two examiners and the examiners’ sessions. All joints were scored in five categories by an experienced examiner according to FCI criteria. The results from examiner 1 were compared between FCI categories. Hips that were assigned an FCI grade of A (n = 19), B (n = 23), C (n = 24), D (n = 24), and E (n = 16) had a mean ± standard deviation FNTi of 0.809 ± 0.024, 0.835 ± 0.044, 0.868 ± 0.022, 0.903 ± 0.033, and 0.923 ± 0.068, respectively (ANOVA, p < 0.05). Therefore, these results show that FNTi is a parameter capable of evaluating proximal femur bone modeling and that it has the potential to enrich conventional CHD scoring criteria if incorporated into a computer-aided diagnosis capable of detecting CHD.

Background Chronic progressive lameness in young dogs which do not show abnormalities on initial, survey imaging, is diagnostically challenging. The present case had undergone extensive diagnostics without definitive diagnosis until magnetic resonance imaging (MRI) allowed lesion detection and localization to the vertebral venous system. Abnormalities of the vertebral venous system have only previously been reported once in veterinary medicine. Case presentation A 1-year-old intact male Standard Poodle was referred with a history of chronic and progressive right thoracic limb lameness. A full work up which included routine haematology, serum biochemistry, radiography, and native computed tomography (CT) of the affected thoracic limb was performed at the referring veterinarian and reported as normal. Management including conventional pain medication, laser treatment, and physiotherapy had been attempted unsuccessfully. Treatment with meloxicam (0.1 mg/kg per os daily) did, however, have some effect on the dog’s pain according to the owner. At our hospital the right thoracic limb lameness was confirmed. A pain reaction was found on manipulation of both shoulders, and a marked muscle atrophy was noted around the right shoulder. On neurological examination, the right thoracic limb had decreased muscle tone and a decreased withdrawal reflex localizing the problem to a right-sided C6-T2 myelopathy or radiculopathy. MRI of the cervicothoracic spine and both brachial plexuses was performed. This showed enlarged internal vertebral venous plexuses especially on the right side from the level of C6 -T2 vertebrae together with marked contrast enhancement of the nerve roots in this region, suggesting nerve root compression secondary to the enlarged venous system. Further analgetic medical management was attempted but due to progression of the dog’s clinical signs, euthanasia was elected 5 months later. Post-mortem investigations confirmed imaging findings and found loss of neurons and swelling of the axons at the compressive sites on histopathology. Conclusions Based on the imaging findings a diagnosis of venous plexus abnormalities with secondary radiculopathy was considered most likely. This was confirmed by the post-mortem investigation. Venous plexus abnormalities are rare but should be considered in young dogs with progressive signs of lameness and regional muscle atrophy in which the underlying cause is not identified with conventional diagnostics.

Accurate radiographic screening evaluation is essential in the genetic control of canine HD, however, the qualitative assessment of hip congruency introduces some subjectivity, leading to excessive variability in scoring. The main objective of this work was to validate a method-Hip Congruency Index (HCI)-capable of objectively measuring the relationship between the acetabulum and the femoral head and associating it with the level of congruency proposed by the Fédération Cynologique Internationale (FCI), with the aim of incorporating it into a computer vision model that classifies HD autonomously. A total of 200 dogs (400 hips) were randomly selected for the study. All radiographs were scored in five categories by an experienced examiner according to FCI criteria. Two examiners performed HCI measurements on 25 hip radiographs to study intra- and inter-examiner reliability and agreement. Additionally, each examiner measured HCI on their half of the study sample (100 dogs), and the results were compared between FCI categories. The paired t-test and the intraclass correlation coefficient (ICC) showed no evidence of a systematic bias, and there was excellent reliability between the measurements of the two examiners and examiners’ sessions. Hips that were assigned an FCI grade of A (n = 120), B (n = 157), C (n = 68), D (n = 38) and E (n = 17) had a mean HCI of 0.739 ± 0.044, 0.666 ± 0.052, 0.605 ± 0.055, 0.494 ± 0.070 and 0.374 ± 0.122, respectively (ANOVA, p < 0.01). Therefore, these results show that HCI is a parameter capable of estimating hip congruency and has the potential to enrich conventional HD scoring criteria if incorporated into an artificial intelligence algorithm competent in diagnosing HD.

Reports of machine learning implementations in veterinary imaging are infrequent but changes in machine learning architecture and access to increased computing power will likely prompt increased interest. This diagnostic accuracy study describes a particular form of machine learning, a deep learning convolution neural network (ConvNet) for hip joint detection and classification of hip dysplasia from ventro‐dorsal (VD) pelvis radiographs submitted for hip dysplasia screening. 11,759 pelvis images were available together with their Fédération Cynologique Internationale (FCI) scores. The dataset was dicotomized into images showing no signs of hip dysplasia (FCI grades “A” and “B”, the “A‐B” group) and hips showing signs of dysplasia (FCI grades “C”, “D,” and “E”, the “C‐E” group). In a transfer learning approach, an existing pretrained ConvNet was fine‐tuned to provide models to recognize hip joints in VD pelvis images and to classify them according to their FCI score grouping. The results yielded two models. The first was successful in detecting hip joints in the VD pelvis images (intersection over union of 85%). The second yielded a sensitivity of 0.53, a specificity of 0.92, a positive predictive value of 0.91, and a negative predictive value of 0.81 for the classification of detected hip joints as being in the “C‐E” group. ConvNets and transfer learning are applicable to veterinary imaging. The models obtained have potential to be a tool to aid in hip screening protocols if hip dysplasia classification performance was improved through access to more data and possibly by model optimization.

Osteogenesis imperfecta (OI) type II is a genetic connective tissue disorder characterized by bone fragility, severe skeletal deformities and shortened limbs. OI usually causes perinatal death of affected individuals. OI type II diagnosis in humans is established by the identification of heterozygous mutations in genes coding for collagens. The purpose of this study was to characterize the pathological phenotype of an OI type II-affected neonatal Holstein calf and to identify the causative genetic variant by whole-genome sequencing (WGS). The calf had acute as well as intrauterine fractures, abnormally shaped long bones and localized arthrogryposis. Genetic analysis revealed a private heterozygous missense variant in COL1A1 (c.3917T>A) located in the fibrillar collagen NC1 domain (p.Val1306Glu) that most likely occurred de novo. This confirmed the diagnosis of OI type II and represents the first report of a pathogenic variant in the fibrillar collagen NC domain of COL1A1 associated to OI type II in domestic animals. Furthermore, this study highlights the utility of WGS-based precise diagnostics for understanding congenital disorders in cattle and the need for continued surveillance for rare lethal genetic disorders in cattle.

The liquid-based Carbo-gel technology enables real-time multimodal image guidance for improved therapeutic precision.