Blood drop patterns and explainable AI reveal livestock anemia signatures
- 1Department of Agricultural Sciences, Fort Valley State University
- 2USDA-ARS, Fruit and Tree Nut Research Station
- 3Institute for Environmental Spatial Analysis, University of North Georgia
Abstract
Anemia significantly limits livestock health and productivity. Existing diagnostic methods remain invasive, infrastructure-dependent, or prone to observer bias. This study evaluated a smartphone-compatible, paper-based blood biosensor combined with machine learning and explainable artificial intelligence for reagent-free image-level anemia classification across goats, sheep, and cattle. For each species, a single drop of whole blood was applied to a glycerol-treated paper substrate, producing bloodspot dispersion patterns associated with packed cell volume (PCV)-defined anemia categories. Standardized smartphone imaging and multi-resolution analysis were used to assess classification performance relative to laboratory hematocrit measurements. Classification performance varied by species, suggesting that species-associated differences in bloodspot morphology and image-pattern consistency may influence the diagnostic information available to image-based models. Sheep showed the highest and most consistent image-level classification performance, while goats and cattle showed more variable species-associated bloodspot patterns. Among the evaluated model configurations, lightweight support vector machine models achieved strong image-level performance with low computational demand. Explainable AI analyses provided complementary evidence that model predictions were associated with spatial bloodspot regions and dominant image-variance components. These findings provide preliminary proof-of-concept evidence for a smartphone-compatible bloodspot imaging framework that may support future development of anemia screening tools for precision livestock management after animal-level and field validation. Because animal-level identifiers were not retained, these results should be interpreted as controlled image-level proof-of-concept performance rather than animal-independent diagnostic accuracy.
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BibTeX
@article{siddique2026blood,
title = {{Blood drop patterns and explainable AI reveal livestock anemia signatures}},
author = {Aftab Siddique and Cornelius Ellington and David Shapiro-Ilan and Cristina Pisani and Sudhanshu S. Panda and Phaneendra Batchu and Brou Kouakou and Thomas H. Terrill},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62774-y},
url = {https://doi.org/10.1038/s41598-026-62774-y},
note = {CC BY},
}
RIS
TY - JOUR
TI - Blood drop patterns and explainable AI reveal livestock anemia signatures
AU - Aftab Siddique
AU - Cornelius Ellington
AU - David Shapiro-Ilan
AU - Cristina Pisani
AU - Sudhanshu S. Panda
AU - Phaneendra Batchu
AU - Brou Kouakou
AU - Thomas H. Terrill
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62774-y
UR - https://doi.org/10.1038/s41598-026-62774-y
AB - Anemia significantly limits livestock health and productivity. Existing diagnostic methods remain invasive, infrastructure-dependent, or prone to observer bias. This study evaluated a smartphone-compatible, paper-based blood biosensor combined with machine learning and explainable artificial intelligence for reagent-free image-level anemia classification across goats, sheep, and cattle. For each species, a single drop of whole blood was applied to a glycerol-treated paper substrate, producing bloodspot dispersion patterns associated with packed cell volume (PCV)-defined anemia categories. Standardized smartphone imaging and multi-resolution analysis were used to assess classification performance relative to laboratory hematocrit measurements. Classification performance varied by species, suggesting that species-associated differences in bloodspot morphology and image-pattern consistency may influence the diagnostic information available to image-based models. Sheep showed the highest and most consistent image-level classification performance, while goats and cattle showed more variable species-associated bloodspot patterns. Among the evaluated model configurations, lightweight support vector machine models achieved strong image-level performance with low computational demand. Explainable AI analyses provided complementary evidence that model predictions were associated with spatial bloodspot regions and dominant image-variance components. These findings provide preliminary proof-of-concept evidence for a smartphone-compatible bloodspot imaging framework that may support future development of anemia screening tools for precision livestock management after animal-level and field validation. Because animal-level identifiers were not retained, these results should be interpreted as controlled image-level proof-of-concept performance rather than animal-independent diagnostic accuracy.
SN - 2045-2322
ER -