Artificial Intelligence in Small Animal Veterinary Medicine

Veterinary Clinics of North America Small Animal Practice · 2026-04-01

Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health

Journal of Global Antimicrobial Resistance · 2025-04-28 · 3 citations

OBJECTIVE: This study aimed to understand the current use of visualizations for multidrug resistance (MDR) data across the One Health spectrum and the visualization preferences and definitions of MDR used by antimicrobial resistance experts, with emphasis on the animal health sector of One Health, which lacks standardized MDR definitions. METHODS: A rapid scoping review was conducted to synthesize current approaches to visualize MDR. Six databases and grey literature were searched with antimicrobial, resistance, surveillance, and figure or dashboard terms. An active machine learning model was used for the initial screening of references. An online survey was distributed to self-identified antimicrobial resistance experts, including questions about respondents' country of employment, job position, definitions of MDR, and preferences for MDR metrics and visualizations. RESULTS: Bar charts, visual antibiograms, heat maps, and network graphs were the most common visualizations employed in peer-reviewed publications, websites, and reports. Survey respondents preferred simplistic visualizations, such as line graphs and heat maps. Respondents used a variety of MDR definitions, although resistance to three or more antimicrobial categories was the most common. Some respondents advocated for the exclusion of intrinsic resistance in the definition, while others argued for its inclusion. CONCLUSIONS: Despite historic proposals for standardizing international definitions of MDR, a lack of consensus remains. Respondents also expressed different preferences for MDR visualizations. Some visualizations currently in use, such as network graphs, are complex and may be challenging to interpret. Harmonization of MDR definitions and optimization of visualizations are essential to facilitate comparisons across populations and studies.

From bark to bytes: artificial intelligence transforming veterinary medicine

American Journal of Veterinary Research · 2025-03-01 · 3 citations

Evidence of transmission and dissemination of diverse <i>bla</i> _NDM-5 -producing <i>Escherichia coli</i> clones between refugee and host communities and their environment: a multicenter cross-sectional study

Applied and Environmental Microbiology · 2025-11-04 · 1 citations

ABSTRACT The global spread of carbapenemase-producing Escherichia coli (CP-Ec) poses a significant public health threat, with particularly severe consequences for vulnerable populations in resource-limited settings. To address this, we conducted in-depth genetic analyses and examined the relatedness of CP-Ec isolates recovered from hospitalized patients, refugees, animals, water, and environmental sources within refugee camps and in marginalized host communities in Lebanon. Nineteen putative CP-Ec isolates, identified by MALDI-TOF MS and designated as community isolates, harbored either NDM ( n = 17) or OXA-48-like ( n = 2) carbapenemases. We used whole-genome sequencing (WGS) to characterize the resistomes and sequence types of these isolates. To further examine genetic relationships and transmission dynamics, we also analyzed publicly available (EnteroBase) CP-Ec genomes from Lebanon ( n = 64) and across the globe ( n = 447 recovered in 2022) alongside 31 additional clinical CP-Ec isolates from the same geographic region. The community isolates belonged to ST10, ST167, ST361, ST410, ST617, ST648, ST940, ST1284, and ST5842. Both community and clinical CP-Ec isolates carried multiple acquired antimicrobial resistance (AMR) genes and chromosomal mutations, with 82% harboring the bla NDM-5 gene. Core-genome SNP analysis showed that refugee isolates clustered with global CP-Ec genomes, highlighting their genomic relatedness and potential for geographical dissemination. Furthermore, integration of our data with previously reported Lebanese genomes demonstrated the spread of bla NDM-5 -carrying E. coli across different hosts and niches, emphasizing the complex interplay of AMR within the human-animal-environment interface. The coexistence of carbapenemase genes with mobile genetic elements that enable horizontal gene transfer raises concerns about the emergence of highly resistant and hypervirulent CP-Ec lineages, especially in vulnerable populations and settings. IMPORTANCE The global rise of CP-Ec strains harboring bla NDM-5 has been increasingly documented in clinical settings. However, little is known about their emergence and transmission in refugee settlements. This study provides a high-resolution genomic characterization of CP-Ec isolated from human, animal, water, and environmental sources in refugee settlements and surrounding host communities. By integrating whole-genome sequencing data from clinical isolates collected in Lebanese hospitals, we reveal genetically related strains in both community and healthcare settings, highlighting the potential introduction of community-acquired strains into clinical environments and vice versa. The widespread detection of bla NDM-5 across multiple reservoirs suggests sustained circulation beyond hospital settings. The identification of CP-Ec in river water used for irrigation and emptying into the Mediterranean Sea highlights wider environmental dimensions that may drive regional dissemination of AMR. Our findings highlight the urgent need for One Health-based AMR surveillance strategies to track the spread of carbapenem-resistant pathogens in high-risk settings.

Characterizing approaches used to display antimicrobial resistance data in veterinary and human medicine: a scoping review

Antimicrobial Stewardship & Healthcare Epidemiology · 2025-01-01

Abstract Introduction: Antimicrobial resistance (AMR) is a complex One Health problem that requires continuous surveillance to minimize the potential hazards. Information must be disseminated promptly in easily understandable formats to support informed decisions and actions by data end-users. One way to address this is through real-time visualizations, such as dashboards, to help key interest-holders understand and monitor AMR. A scoping review was conducted to understand the current body of evidence surrounding real-time AMR visualizations in both veterinary and human health. Methods: Twelve sources were searched for relevant citations. 1763 citations were included in the screening process. Citations were screened for four main criteria: (i) the text had to be a primary research article in English (ii) published between 1990 and 2023, and (iii) it had to discuss the methodology of an AMR display (iv) that was updated at least quarterly. Results: Forty-two publications were identified as relevant. Publication information, information about the data used in the described displays, display information, and user information were charted. Publications were from 25 countries and utilized data from over 40 databases. Various bacterial genera and species were reported; the most common bacterial species were Escherichia coli and Staphylococcus aureus . Displays were most focused mainly on human data. Conclusions: AMR data visualization has been implemented globally and is a critical component of continued AMR surveillance. Displays are often part of a larger surveillance system. A key challenge is designing a visualization for an intended audience and the information then being utilized by that audience.

Antimicrobial use regulations are associated with increased susceptibility among bovine Salmonella isolates from a U.S. surveillance system

One Health · 2025-01-31

Health authorities around the world have called to limit antimicrobial use in food-producing animals. In the United States, two recent regulatory actions have changed the use of antimicrobials in livestock, banning production uses in 2017 and restricting extra-label use of cephalosporins in 2012. This study aimed to assess the impact of the 2012 and 2017 regulations on antimicrobial use in cattle in the United States by analyzing 18,627 bovine Salmonella AMR susceptibility patterns using data from the National Antimicrobial Resistance Monitoring System (NARMS). Logistic regression was used to model the odds of being a susceptible isolate. Additionally, interval-censored accelerated failure time (AFT) models were used to analyze changes in minimum inhibitory concentrations (MICs) over time and by serotype. The most common serotypes were Montevideo ( n = 3003), Anatum ( n = 1394), Cerro ( n = 1373), and Typhimurium ( n = 1213). Susceptibility was highest for azithromycin (99 %), ciprofloxacin, gentamicin, and trimethoprim-sulfamethoxazole (all 98 %), and lowest for tetracycline (76 %), chloramphenicol (86 %), and ampicillin (85 %). Serotypes Typhimurium, Newport, and Dublin exhibited lower susceptibility compared to other serotypes. Susceptibility to all antimicrobials increased during the periods 2013–2017 and 2018–2022 compared to isolates before 2012, with a greater increase in 2018–2022. MICs decreased for most antimicrobials except for chloramphenicol and gentamicin, which showed increased median MIC for the periods 2013–2017 and 2018–2022, respectively. In conclusion, antimicrobial use restrictions appear correlated with a reduction in Salmonella AMR, although this effect cannot be untangled from the effect of time in this dataset. • Increased antimicrobial susceptibility in Salmonella after antimicrobial regulation. • Greater increase in susceptibility and MIC reduction in 2018–2022 than 2013–2017. • Salmonella serotype is strongly associated with antimicrobial resistance.

Antimicrobial minimum inhibitory concentrations can be imputed from phenotypic data using a random forest approach

American Journal of Veterinary Research · 2025-02-27 · 2 citations

Objective: Antimicrobial resistance (AMR) is a public health threat requiring monitoring across multiple sectors because AMR genes and pathogens can pass between humans, animals, and the environment. Idiosyncrasies in AMR data, including missing data and changes in testing protocols, make characterizing AMR trends over time and sectors challenging. Therefore, this study applied machine learning methods to impute missing minimum inhibitory concentrations. Methods: Models were built using cattle-associated Escherichia coli from the National Antimicrobial Resistance Monitoring System. Random forest models were designed to predict the minimum inhibitory concentration of a given E coli isolate for 10 antimicrobials. Predictors included isolate metadata and the minimum inhibitory concentrations of other antimicrobials. Model performance was evaluated on held-out test data and 2 external datasets (E coli isolated from chickens and humans). Results: Overall, the accuracy within 1 minimum inhibitory concentration category was over 80% for all 10 antimicrobials and over 90% for 5 antimicrobials on test data. Six of the models performed as well on both external datasets as on test data, whereas the remaining 4 had similar accuracy on the human dataset but lower on the chicken data. Conclusions: These results indicate that the models can predict minimum inhibitory concentration values at a level of accuracy that would be helpful for imputation in resistance datasets. Clinical Relevance: The imputation of missing minimum inhibitory concentrations would allow for better evaluation of AMR trends over time, helping inform stewardship policies. These models may also help streamline surveillance and clinical susceptibility testing because they suggest which antimicrobials need to be laboratory-tested and which can be extrapolated by modeling.

The impact of antimicrobial use regulations on antimicrobial resistance among Salmonella isolates from bovine samples submitted to a veterinary diagnostic laboratory in Central New York

One Health · 2025-06-01

In recognition that antimicrobial resistance in human pathogens may stem from antimicrobial use in agricultural settings, the United States Food and Drug Administration (FDA) ordered restrictions on antimicrobial usage (AMU) in food-producing animals. In 2012 the FDA restricted the extra-label use of third-generation cephalosporins, and in 2017 the FDA mandated veterinary oversight for the use of antimicrobials in the feed and water of food-producing animals and eliminated production-related uses. However, the impact of these restrictions on the antimicrobial resistance status of important pathogens, such as Salmonella , remains unclear. To address this gap in knowledge, we analyzed veterinary diagnostic laboratory data on 2413 Salmonella isolates from submitted bovine samples. We fitted logistic regression models to evaluate changes in proportions of antimicrobial-resistant isolates, and we used accelerated failure time (AFT) models to determine changes in minimum inhibitory concentration (MIC) values. Our analysis revealed the 2012 AMU restriction to be associated with a decrease in the odds of resistance to chlortetracycline (OR = 0.49; 95 % CI = 0.28–0.86), oxytetracycline (OR = 0.47; 95 % CI = 0.27–0.82), and neomycin (OR = 0.45; 95 % CI = 0.25–0.80). Furthermore, we found significant decreases in MIC values for chlortetracycline (CR = 0.74; 95 % CI = 0.62–0.87) and oxytetracycline (CR = 0.64; 95 % CI = 0.56–0.73) for the same AMU restriction. We found a significant association between the 2017 AMU restriction and decreased odds of resistance to florfenicol (OR = 0.28; 95 % CI = 0.09–0.92). Salmonella serotype was an important predictor of resistance to all antimicrobials assessed via logistic regression or AFT models. Overall, our study suggests that in the region served by the laboratory, AMU restrictions have either had no detectable effect or are associated with decreasing AMR and MIC trends for Salmonella isolated from bovine samples, depending on the antimicrobial.

Retrospective analysis of antimicrobial susceptibility testing illustrates the problem of resistant Staphylococcus species in cats in the northeastern United States

Preventive Veterinary Medicine · 2025-08-25

Analyzing multidrug resistance patterns across the food supply chain using association rule mining

Preventive Veterinary Medicine · 2025-10-07

We used the machine learning method association rule mining to analyze multidrug resistance (MDR) among cattle-associated Escherichia coli along the food supply chain in the USA. All datasets were stratified by year, source, and resistance indicator (genotypic/phenotypic). Pruned rulesets were compared by calculating the proportion of rules from a comparison ruleset that are captured in a reference ruleset. Rulesets were compared across years within each source and indicator type to quantify how MDR patterns change over time. At the class level, on average nearly 50 % or more of the MDR patterns remain the same year over year for genotypic and phenotypic indicators. Rulesets were compared between data sources to quantify how MDR patterns change across the food supply chain. These comparisons suggest that there is a greater diversity of MDR patterns present at slaughterhouse settings than at retail settings; and further, that there is a greater diversity of MDR patterns amongst sick cattle on farm settings than at either slaughterhouse or retail settings. Genetic evidence supports this being attributable to a greater genetic diversity associated with pathogenic bacteria vs commensals. Rulesets were compared between indicators to quantify the degree of correspondence between phenotypic and genotypic data. Genotypic rulesets were better able to capture phenotypic rulesets than the reverse. Adding another aminoglycoside (streptomycin) to the phenotypic analysis, improved ruleset correspondence. This asymmetry may be driven by drug specific aminoglycoside resistance genes, suggesting that more drugs need to be assessed to have a fuller understanding of the variation in MDR patterns.