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

A rapid, sensitive, user-friendly, and deployable deep learning tool was developed for classifying LFA test images to enhance diagnosis and reporting, particularly in settings with limited laboratory resources.

Authors

Doina Caragea (ORCID: https://orcid.org/0000-0002-6440-0914)
Aliva Bakshi
Jishu N Shi (ORCID: https://orcid.org/0000-0002-7177-9280)
Lihua Wang (ORCID: https://orcid.org/0000-0001-7951-4175)
Laura C. Miller (ORCID: https://orcid.org/0000-0002-8946-9416)
Jake Stetson

Institutions

Kansas State University (US)

Publication Details

Journal: American Journal of Veterinary Research
Published: 2025-02-28
DOI: https://doi.org/10.2460/ajvr.24.10.0305
Citations: 4
Primary Topic: Animal Disease Management and Epidemiology
Type: article
Field-Weighted Citation Impact: 7.61

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article

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

Doina Caragea, Aliva Bakshi, Jishu N Shi, Lihua Wang et al.

Feb 28, 2025

4 citations

American Journal of Veterinary Research

Animal Disease Management and Epidemiology

7.61

article

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

Doina Caragea, Aliva Bakshi, Jishu N Shi, Lihua Wang, Laura C. Miller, Jake Stetson

February 28, 2025article en

4 citations

Abstract

American Journal of Veterinary ResearchVol. 86(S1)

Kansas State University (US)

Quality Education

Openalex Percentile: Top 4%

Animal Disease Management and Epidemiology

7.61

DOI Open Access PDF OpenAlex

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Toward a rapid, sensitive, user-friendly, field-deployable artificial intelligence tool for enhancing African swine fever diagnosis and reporting — Doina Caragea, Aliva Bakshi, et al. · American Journal of Veterinary Research (2025) | TGRS Research Map | TGRS