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Peer-reviewedCC BYTropical animal health and production

Routine serum biomarkers and lightgbm modeling for the diagnosis of abomasal ulcers in culled dairy cows.

  • J E Amorim1
  • K G Perestrelo2
  • A C Pinheiro2
  • J M N Freitas2
  • T V Q Lancheros2
  • C S Mori2
  • F Ferreira3
  • M C A Sucupira2
  1. 1Department of Clinical Medicine, School of Veterinary Medicine and Animal Science, University of São Paulo, Av. Prof. Dr. Orlando Marques de Paiva 87, Cidade Universitária, São Paulo, São Paulo State, 05508-270, Brazil. [email protected].
  2. 2Department of Clinical Medicine, School of Veterinary Medicine and Animal Science, University of São Paulo, Av. Prof. Dr. Orlando Marques de Paiva 87, Cidade Universitária, São Paulo, São Paulo State, 05508-270, Brazil.
  3. 3Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine and Animal Science, University of São Paulo, Av. Prof. Dr. Orlando Marques de Paiva 87, Cidade Universitária, São Paulo, São Paulo State, 05508-270, Brazil.
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Abstract

Abomasal ulcer (AU) is a common condition in dairy cows that significantly affects productivity and longevity. This study evaluated the diagnostic value of serum biomarkers and the fecal occult blood test (FOBT), and tested a Light Gradient Boosting Machine (LightGBM) model to predict ulcer occurrence. Postmortem analyses were performed on 304 culled dairy cows, from which blood and fecal samples were collected. Animals were classified according to the presence (n = 249) or absence (n = 55) of abomasal ulcers. No individual biomarker, including gastrin or pepsinogen, demonstrated consistent diagnostic value. The FOBT showed high specificity (90.9%) but low sensitivity (27.7%), while the LightGBM model showed limited discriminatory performance (AUC ROC = 0.60). The model identified creatinine, platelet count, GGT, mean corpuscular hemoglobin, and total protein as relevant features. These findings indicate that current serum biomarkers and routine blood variables are insufficient for accurate individual diagnosis of AU, underscoring the need for more specific diagnostic tools.

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Cite

BibTeXRIS
BibTeX
@article{amorim2026routine,
  title = {{Routine serum biomarkers and lightgbm modeling for the diagnosis of abomasal ulcers in culled dairy cows.}},
  author = {J E Amorim and K G Perestrelo and A C Pinheiro and J M N Freitas and T V Q Lancheros and C S Mori and F Ferreira and M C A Sucupira},
  journal = {Tropical animal health and production},
  year = {2026},
  month = oct,
  doi = {10.1007/s11250-026-05432-9},
  url = {https://europepmc.org/article/MED/42902090},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Routine serum biomarkers and lightgbm modeling for the diagnosis of abomasal ulcers in culled dairy cows.
AU  - J E Amorim
AU  - K G Perestrelo
AU  - A C Pinheiro
AU  - J M N Freitas
AU  - T V Q Lancheros
AU  - C S Mori
AU  - F Ferreira
AU  - M C A Sucupira
PY  - 2026
DA  - 2026/10/20
JO  - Tropical animal health and production
DO  - 10.1007/s11250-026-05432-9
UR  - https://europepmc.org/article/MED/42902090
AB  - Abomasal ulcer (AU) is a common condition in dairy cows that significantly affects productivity and longevity. This study evaluated the diagnostic value of serum biomarkers and the fecal occult blood test (FOBT), and tested a Light Gradient Boosting Machine (LightGBM) model to predict ulcer occurrence. Postmortem analyses were performed on 304 culled dairy cows, from which blood and fecal samples were collected. Animals were classified according to the presence (n = 249) or absence (n = 55) of abomasal ulcers. No individual biomarker, including gastrin or pepsinogen, demonstrated consistent diagnostic value. The FOBT showed high specificity (90.9%) but low sensitivity (27.7%), while the LightGBM model showed limited discriminatory performance (AUC ROC = 0.60). The model identified creatinine, platelet count, GGT, mean corpuscular hemoglobin, and total protein as relevant features. These findings indicate that current serum biomarkers and routine blood variables are insufficient for accurate individual diagnosis of AU, underscoring the need for more specific diagnostic tools.
SN  - 0049-4747
SN  - 1573-7438
ER  -