Toward a rapid, sensitive, user-friendly, field-deployable artificial intelligence tool for enhancing African swine fever diagnosis and reporting.

Journal: American journal of veterinary research
PMID: 40023145

Abstract

OBJECTIVE: African swine fever (ASF) is a lethal and highly contagious transboundary animal disease with the potential for rapid international spread. Lateral flow assays (LFAs) are sometimes hard to read by the inexperienced user, mainly due to the LFA sensitivity and reading ambiguities. Our objective was to develop and implement an AI-powered tool to enhance the accuracy of LFA reading, thereby improving rapid and early detection for ASF diagnosis and reporting.

Authors

  • Aliva Bakshi
    Department of Computer Science, Carl R. Ice College of Engineering, Kansas State University, Manhattan, KS.
  • Jake Stetson
    Department of Computer Science, Carl R. Ice College of Engineering, Kansas State University, Manhattan, KS.
  • Lihua Wang
    Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China.
  • Jishu Shi
    Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS.
  • Doina Caragea
  • Laura C Miller
    Department of Diagnostic Medicine/Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS.

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