Artificial intelligence (AI) serves as a decision support tool, not a replacement for clinical judgment, when used to interpret radiological images. Veterinarians retain full professional accountability for all diagnoses and treatment decisions, regardless of AI involvement. Transparency is essential: if you cannot explain to clients in understandable terms how an AI system works and its limitations, it should not be used in practice. Successful implementation requires following established best practices, including comprehensive team training, maintaining traditional diagnostic skills, and establishing quality assurance protocols.

A heart-convolutional neural network (heart-CNN) was developed and tested for the automatic detection of left atrial enlargement (LAE) from feline thoracic radiographs. A retrospective and multicenter study was performed. Right lateral and dorso-ventral and/or ventro-dorsal thoracic radiographs of cats with concomitant echocardiographic examination were selected from the internal databases of both academic and private referral institutions. Radiographic images were classified as no LAE, mild, moderate and severe LAE, based on echocardiographic reports. Heart-CNN performance was evaluated using confusion matrices and receiver operating characteristic curves for both radiographic projections considering a multiclass and a binary classification. Considering the multiclass classification, for the right lateral view, the area under the curve (AUC) was of 0.73, 0.68, 0.64 and 0.78 for the no LAE, mild, moderate and severe LAE groups, respectively. The AUCs for the dorso-ventral and/or ventro-dorsal images were 0.73, 0.64, 0.63 and 0.76 for the no LAE, mild, moderate and severe LAE groups, respectively. In the binary classification, AUCs were 0.83 and 0.81 for right lateral and dorso-ventral and/or ventro-dorsal projections, respectively. The developed AI-based tool seems to be a promising support for automatic identification of more advanced stages of LAE in cats.

The aim of this study is to investigate how well veterinary institutions have adopted to the structure naming conventions provided by the American Association of Physicists in Medicine (AAPM) Task Group 263 (TG-263). The TG-263 standard nomenclature has been suggested as a means of reducing the uncertainty in structure definitions in radiation oncology. Our interest is in understanding whether these recommendations have been adopted in the veterinary radiation oncology setting. This study includes a convenience sample survey of five veterinary institutions across the United States, each with 38-102 total structure names defined across their templates. The data were examined in two steps: an automated check that flagged forbidden characters, spaces, and exceeding length, and a manual review for abbreviations (e.g., L/R), uniqueness, and mapping to the TG-263 structure spreadsheet and SNOMED (Systematized Nomenclature of Medicine) Veterinary Extension. Each structure was classified as compliant, having one error, multiple errors, or uniquely defined by the institution. The results were analyzed by determining the percentages of each category per institution. Our results suggest wide variability with a mean compliance of 55.2% and a standard deviation of 35.2%, which suggests no clear trend.

Hock scoring in dairy cattle is a crucial welfare assessment tool used to evaluate the condition of a cow's hocks, particularly for signs of injury, swelling, or lesions. These scores provide insight into the overall well-being of the animals and are essential for ensuring proper management and housing conditions. Accurate hock scoring is vital because it can indicate issues such as poor bedding quality or inadequate space, which directly affect the health and productivity of the herd. Traditionally, hock scoring is performed manually by trained observers. However, consistency in scoring can be a challenge. Two studies were conducted to quantify inconsistency in hock scoring. In one study, manual scoring reproducibility was measured. In the second study, manual and video scoring repeatability was measured. Repeatability was quantified with a weighted Cohen's kappa metric. Manual scoring was found to be inconsistent but more consistent than video scoring. This variability highlights the need for a more reliable, objective method of scoring. To address this, we explored the automation of hock score detection using artificial intelligence. Specifically, we employed a simple U-net semantic segmentation algorithm to detect wounds on the hocks without classifying them into specific categories. Automating the detection process can reduce observer bias, improve consistency, and allow for continuous monitoring of large herds. This approach holds promise for enhancing animal welfare by providing a more efficient and accurate method of assessing hock health in dairy cattle.

Objective:To develop a machine learning (ML) model to identify fiducial points on canine ECGs to localize right-sided accessory pathways as posterior or anterior during ventricular preexcitation (VPE). Methods:ECG recordings with VPE and documented accessory pathway locations were preprocessed for a 1-dimensional U-net algorithm. A web-based platform (https://setpsi.com/AccessoryPathways/) was created. Training used approximately 70% of pooled beats from 16 of 27 dogs. Testing used approximately 30% of pooled beats from 11 of 27 dogs to assess accurate diagnosis of posterior versus anterior accessory pathways. Vectorcardiograms and mean electrical axis (MEA) were calculated to validate the ML model. Fiducial boundary correctness and beat identification accuracy were assessed with receiver operator characteristic curves and reported as area under the curve (AUC; 95% CI). A Mann-Whitney test was used to compare MEA methods (median; IQR). Results:The ML algorithm was trained on 3,405 beats and tested on 1,984 beats. The model identified fiducial points P wave to delta wave (AUC, 0.957; 95% CI, 0.957 to 0.958) and delta wave/QRS (AUC, 0.965; 95% CI, 0.964 to 0.966), classified individual beats as posterior (AUC, 0.917; 95% CI, 0.915 to 0.920) and anterior (AUC, 0.948; 95% CI, 0.947 to 0.949), and determined pathway location in 82% (9 of 11) of test dogs. Vectorcardiograms of posterior pathways showed oval or elliptical loops with superior leftward vectors, while anterior pathways displayed complex figure-eight loops with inferior leftward vectors. The MEA differed (P < .01) between posterior (-23.7°; IQR, 39.5°) and anterior (61.1°; IQR, 9.8°) pathways. Both methods validated the ML model. Conclusions:The ML model accurately localized accessory pathways in canine VPE. Clinical Relevance:ML will advance the ability to accurately diagnose VPE.

Radiomics involves quantitative analyses over specified regions to extract datapoints and quantitative features which can be used to train artificial intelligence models. Assessment of observer variability is performed to ensure reliability and reproducibility in the extracted quantitative features. Our primary objective is to compare the interobserver and intraobserver coefficients between participants in contouring computed tomographic images using canine acanthomatous ameloblastoma as a representative model. Ten canine acanthomatous ameloblastoma cases were randomly selected from Cornell University Hospital for Animals’ computed tomography database from 2014 to 2021 based on tumor visibility and availability of non-contrast and contrast enhanced images with bone and soft tissue reconstructions. These cases were evaluated and manually contoured in 3DSlicer by three specialists across two randomized sittings with instructions provided to guide contouring. Interobserver and intraobserver correlation coefficients for centroid coordinates and volumes indicated excellent agreement. Intraobserver Dice Similarity Index (DI) had moderate agreement (mean = 0.78±0.11) while interobserver DI was poor (mean = 0.69±0.14). Intraobserver mean Hausdorff Distances at 0.80±0.48 mm and interobserver mean Hausdorff Distances at 1.21±0.66 mm, indicating variability in contour comparisons between participants. Interobserver volume measurements were statistically different (P < 0.001), with moderate effect sizes. The excellent agreement in centroid coordinates and volumes supports consistent and reliable target region definition. However, there were statistically significant variabilities within the contour comparisons. The DI and Hausdorff Distances had overall moderate agreement; however, there was poor agreement with interparticipant DI, indicating less consistency between participants. The observed variability underscores the need for standardized contouring models to enhance reproducibility and reliability for clinical integration.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative potential of AI in veterinary diagnostic imaging and radiation oncology. This position statement outlines the guiding principles for the ethical development and integration of AI technologies to ensure patient safety and clinical effectiveness. Artificial intelligence systems must adhere to good machine learning practices, emphasizing transparency, error reporting, and the involvement of clinical experts throughout development. These tools should also include robust mechanisms for secure patient data handling and postimplementation monitoring. The position highlights the critical importance of maintaining a veterinarian in the loop, preferably a board-certified radiologist or radiation oncologist, to interpret AI outputs and safeguard diagnostic quality. Currently, no commercially available AI products for veterinary diagnostic imaging meet the required standards for transparency, validation, or safety. The ACVR and ECVDI advocate for rigorous peer-reviewed research, unbiased third-party evaluations, and interdisciplinary collaboration to establish evidence-based benchmarks for AI applications. Additionally, the statement calls for enhanced education on AI for veterinary professionals, from foundational training in curricula to continuing education for practitioners. Veterinarians are encouraged to disclose AI usage to pet owners and provide alternative diagnostic options as needed. Regulatory bodies should establish guidelines to prevent misuse and protect the profession and patients. The ACVR and ECVDI stress the need for a cautious, informed approach to AI adoption, ensuring these technologies augment, rather than compromise, veterinary care.

Objective . β -emitting radionuclides, such as 90 Sr 90 Y, are widely used in clinical settings for the treatment of both benign and malignant lesions, particularly as surface applicators. Despite their clinical relevance, the three-dimensional dose distributions delivered by these applicators remain inadequately characterized using Monte Carlo simulations, the current gold standard for dose calculation. This study aims to address these limitations by characterizing the three-dimensional dose distribution of a commonly used 90 Sr 90 Y Pterygium applicator. The goals include generating accurate percent-depth-dose (PDD) curves and validating a custom irradiation setup using radiochromic film and Monte Carlo simulations to enable accessible, reproducible, and highly precise radiobiology experiments. Approach . A Monte Carlo dose calculation software based on Geant4 10.02.p02 was developed, and the Amersham SIA 20 Pterygium applicator, a stacked film setup with 30 EBT-XD GafChromic ® films, and a film-cell irradiation setup (a film layer, cell monolayer, and growth media) were modeled. The dose rate was averaged over the 8.2 mm diameter active area on the surface in water in both the stacked film setup and the film-cell setup. The spectrum of the source was also calculated. An experimental PDD was generated by irradiating stacked films and was compared to the Monte Carlo simulations. Main results . The measured and computed PDDs agreed within 2% within 2.6 mm of depth. The dose rates were 28.30, 26.48, 21.23, and 22.76 cGy s −1 on the surface in water, in the film active layer, in the cell monolayer, and in the growth media, respectively, compared to the manufacturer’s nominal value of 27 cGy s −1 . Significance . A Monte Carlo-validated PDD curve of the source was generated in a stacked film setup using EBT-XD GafChromic ® film. A custom film-cell irradiation setup was characterized for future radiobiology experiments.

BACKGROUND:A central challenge in classical radiobiology experiments, where dishes are plated with cells and then irradiated, is that the radiation dose deposited to the cells is often subjected to systematic and random errors. One method to validate the dose to cells is to use radiochromic film underneath or above the plated cells to estimate the dose given to the culture. PURPOSE:To explore the feasibility of seeding cells directly onto radiochromic film, enabling precise characterization of cellular responses to microscopic fluctuations in radiation intensity. This technique decreases the challenge of registration, improving the correlation between individual cell and their received radiation dose. METHODS:We investigate several adhesives and strategies for adhering spindle cells on a thin layer atop the film. After finding a robust approach, we develop a novel strategy for absolute dose calibration to the cell-film substrates. Finally, we evaluate this approach using a standard radiobiology assay by exposing cell films to uniform doses and comparing the cell colony survival fraction with established and published data. RESULTS:Easily obtainable gelatin and 3D printing adhesives provide substrates and adhesives for cell colony formation. Calibration and sample film data from traditional flatbed scanners used for film dosimetry, confocal microscopes, and a novel calibration method can be used to measure the dose to the films. CONCLUSIONS:Using a film-to-dose calibration methodology, we show that cell colony assay experiments can be conducted directly on radiochromic film without significant spatial variation.

Dairy owners spend significant effort to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce these costs, yet obstacles arise when adapting advanced tools to farming environments. In this work, we adapt AI tools to dairy cow teat localization, teat shape, and teat skin condition classifications. We also curate a data collection and analysis methodology for a Machine Learning (ML) pipeline. The resulting teat shape prediction model achieves a mean Average Precision (mAP) of 0.783, and the teat skin condition model achieves a mean average precision of 0.828. Our work leverages existing ML vision models to facilitate the individualized identification of teat health and skin conditions, applying AI to the dairy management industry.

Canine appendicular osteosarcoma (OSCA) is a highly aggressive cancer, constituting 85% of all bone tumors in dogs, predominantly affecting larger breeds and exhibiting a high metastatic rate. This disease also shares many genomic similarities with human osteosarcomas, making it an ideal comparative model for treatment discovery. In this study, we characterized the radiobiological properties of several OSCA cell lines when subjected to spatially fractionated radiation therapy (SFRT) and chemotherapy. Specifically, we focused on lower (peak) doses from SFRT ranging from 1 to 10 Gy. These canine OSCA cell lines serve as useful models for osteosarcoma research that can be utilized to find translational treatments for both canine and human patients. This study reaffirms established clinical wisdom regarding the notoriously radioresistant profile of osteosarcomas but additionally offers compelling evidence supporting SFRT as a promising treatment option that could be used in conjunction with other cytotoxic agents.

Mastitis is among the costliest diseases affecting dairy cows, partly due to the resulting permanent reduction in the quantity and quality of milk produced. Most mastitis cases involve pathogenic organisms entering the cow's mammary gland through the teat canal. The teat has natural defenses against these pathogens that can be disrupted during milk harvesting. These disruptions of the teat's circulatory system and tissue integrity can predispose them to mastitis. Traditionally, machine milking-induced changes in teat blood circulation and tissue integrity have been assessed by means of manual evaluation and ultrasonography. Infrared thermography has previously been shown to produce precise and consistent measurements of skin surface temperatures (SST) on cows' hind teats. Our objective was to describe the variability in the teat SST following machine milking. Describing the variability in teat SST before and after milking could be useful to guide further studies to elucidate the physiology of the effects of milking on teat defense mechanisms. In this observational study, thermographic images of both hind teats from 140 cows immediately pre- and postmachine milking were analyzed. The average SST were subsequently determined at the proximal, middle, and distal aspects of each hind teat using image analysis software. The LSM (95% CI) from general linear mixed models of the pre- and postmilking SST, respectively, were 33.6 (33.5-33.8)°C and 35.4 (35.3-35.5)°C at the proximal aspect, 33.2 (33.1-33.4)°C and 35.2 (35.1-35.3)°C at the middle aspect, and 32.3 (32.1-32.5)°C and 34.0 (33.9-34.1)°C at the distal aspect. The observed increase in SST from pre- to postmilking SST at all 3 aspects of the teat suggest that some of the variability in the SST can be attributed to the milking event. Future research is warranted to investigate the biological relevance of SST changes during machine milking and any potential change in teat defense mechanisms, risk of mastitis, or other pathologies.

Since 2010, there has been little published data on the state of equipment and infrastructure in veterinary radiation oncology clinical practice. These data are important not only to identify the status and use of technology within the veterinary radiation oncology community but also to help identify the extent of medical physics support. The purpose of our study is to report findings from a survey of veterinary radiation oncologists in the USA, Canada, and select centers outside of North America in 2022. A 40-question survey covering topics such as type of radiotherapy equipment, techniques offered, treatment planning systems and dose calculation algorithms, special techniques, board-certified radiation oncologists and residents, and extent of medical physics support was distributed through an online survey tool. Results from 40 veterinary radiation oncology institutions, with equipment explicitly used for veterinary care, suggest that the current state of practice is not dissimilar to what currently exists in human radiation oncology facilities; techniques and technologies commonly employed include flattening filter-free mode megavoltage beams, volumetric arc therapy, daily cone-beam computed tomography, image-guided radiation therapy, and sophisticated dose calculation algorithms. These findings suggest the need for modern radiation oncology acceptance testing, commissioning, and quality assurance programs within the veterinary community. The increase in veterinary radiation oncology residency positions and increasing sophistication of equipment suggests that increased levels of standardized medical physics support would benefit the veterinary radiation oncology community.

The importance of teat canal integrity and its adjacent tissues in the dynamics of IMI is well documented, whereas research on the relationship between teat skin condition and clinical mastitis occurrence is scarce. The objective of this prospective cohort study was to investigate the association of teat skin condition with clinical mastitis occurrence in a closed cohort from a commercial dairy farm with a thrice daily milking schedule in the Northeast United States. We tested the hypothesis that quarters with teats with altered skin condition would have higher odds of clinical mastitis than those with normal skin. Teat skin condition from 2,670 cows was assessed during a single visit and categorized into (1) normal, (2) dry skin, (3) skin lesion, and (4) dry skin and skin lesion. Cows were monitored for 2 wk after the teat skin condition assessment, and the occurrence of clinical mastitis at the quarter level was documented. A generalized linear mixed model with a logit link and a binomial distribution revealed an association between teat skin condition and the occurrence of clinical mastitis. Compared with quarters with teats with normal teat skin, the odds (95% CI) of clinical mastitis were 0.98 (0.60-1.60) for teats with dry skin, 1.88 (0.97-3.66) for teats with a skin lesion, and 4.87 (1.71-13.85) for teats with dry skin and a skin lesion. We conclude that quarters from teats with dry skin and skin lesions had higher odds of clinical mastitis. In addition, we found evidence that quarters with teats with skin lesions have higher odds of clinical mastitis than those with normal teat skin, though future studies are needed. The results from this study show that teat skin condition should be considered in mastitis control programs on dairy operations.

Artificial intelligence (AI) is rapidly developing as an important aspect of diagnostic imaging workflows in both human and veterinary medicine . AI can reshape aspects of care, but it is challenged by ongoing infrastructure limitations, appropriate development of use cases, and critical review of AI tools for veterinary imaging. AI can aid many aspects of the diagnostic imaging workflow including image acquisition, workflow optimization, computer-aided diagnosis, radiomic analysis, and other predictive modeling. This article describes the current state of AI in veterinary diagnostic imaging and establishes limitations and opportunities for developing this important and novel technology.

Purpose To create an open-access Linear Accelerator Education and Augmented Reality Navigator (Open LEARN) via 3D printable objects and interactive augmented reality assets. Methods This study describes an augmented reality linear accelerator (linac) model accessible through a QR code and a smartphone to address the challenges of medical physics and radiation oncology trainees in low-to-middle-income countries. Results Major components of a generic linear accelerator are modeled as individual objects. These objects can be 3D printed for hands-on learning and used as interactive 3D assets within the augmented reality app. In the AR app, descriptions are displayed to navigate the components spatially and textually. Items modeled include the treatment couch, klystron, circulator, RF waveguides, electron gun, waveguide, beam steering assemblies, target, collimators, multi-leaf collimators, and imaging systems. The linear accelerator is rendered at nearly 100% of its actual size, allowing users to change magnification and view objects from different angles. Conclusions The augmented reality linear accelerators and 3D-printed objects make these complex machines easily accessible with smartphones and 3D-printing technologies, facilitating education and training through physical and virtual interaction.

This report describes a comprehensive framework for applying artificial intelligence (AI) in veterinary medicine. Our framework draws on existing research on AI implementation in human medicine and addresses the challenges of limited technology expertise and the need for scalability. The critical components of this framework include assembling a diverse team of experts in AI, promoting a foundational understanding of AI among veterinary professionals, identifying relevant use cases and objectives, ensuring data quality and availability, creating an effective implementation plan, providing team training, fostering collaboration, considering ethical and legal obligations, integrating AI into existing workflows, monitoring and evaluating performance, managing change effectively, and staying up-to-date with technological advancements. Incorporating AI into veterinary medicine requires addressing unique ethical and legal considerations, including data privacy, owner consent, and the impact of AI outputs on decision-making. Effective change management principles aid in avoiding disruptions and building trust in AI technology. Furthermore, continuous evaluation of AI's relevance in veterinary practice ensures that the benefits of AI translate into meaningful improvements in patient care.