ORIGINAL RESEARCH article
Front. Vet. Sci.
Sec. One Health
A Data Privacy and Deep Learning based AMR Dashboard for Rural and Regional Veterinary Practices in Texas
Provisionally accepted
- 1Texas Tech University Edward E Whitacre Junior College of Engineering, Lubbock, United States
- 2Texas Tech University School of Veterinary Medcine, Amarillo, United States
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Antimicrobial resistance (AMR) grows as a major worldwide health threat which affects treatment of both human and veterinary medicine. Practitioners working in rural and regional veterinary offices throughout Texas face difficulties obtaining real-time data tools which support making antimicrobial treatment decisions. This study introduces an AI-driven dashboard to address veterinary medicine needs by utilizing real-world AMR data collected over 14 years from veterinary labs throughout Texas. The dashboard employs deep learning models along with Long Short-Term Memory (LSTM) and Prophet with Recurrent Neural Networks (RNN) for prediction tasks and data imputation so practitioners can access insights utilizing visual elements such as heatmaps, Sankey plots and MIC distributions and susceptibility tables. The dashboard empowers veterinarians with predictive analytics to perform empirical treatment selection and monitor resistance patterns to improve antimicrobial stewardship. Additionally, the dashboard integrates privacy-preserving fingerprinting techniques using steganographic marks, ensuring secure data sharing without compromising utility. Our novel approach addresses critical gaps in veterinary AMR data analysis, supporting antimicrobial stewardship and public health efforts through One Health frameworks. The findings demonstrate AI has proven its capacity to transform evidence-based veterinary medicine through data integrity and privacy.
Keywords: Antimicrobial resistance (AMR), Data privacy, deep learning, fingerprinting, Long Short Term Memory (LSTM), Prophet, time series
Received: 13 Jun 2025; Accepted: 12 Nov 2025.
Copyright: © 2025 Gopali, Ji, Schmidt, Zimmerman and Awosile. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tianxi Ji, tiji@ttu.edu
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