{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Agricultural Sciences, Fort Valley State University",
    "USDA-ARS, Fruit and Tree Nut Research Station",
    "Institute for Environmental Spatial Analysis, University of North Georgia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Aftab Siddique"
    },
    {
      "affs": [
        0
      ],
      "name": "Cornelius Ellington"
    },
    {
      "affs": [
        1
      ],
      "name": "David Shapiro-Ilan"
    },
    {
      "affs": [
        1
      ],
      "name": "Cristina Pisani"
    },
    {
      "affs": [
        2
      ],
      "name": "Sudhanshu S. Panda"
    },
    {
      "affs": [
        0
      ],
      "name": "Phaneendra Batchu"
    },
    {
      "affs": [
        0
      ],
      "name": "Brou Kouakou"
    },
    {
      "affs": [
        0
      ],
      "name": "Thomas H. Terrill"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0w52fd6cdev42.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62774-y",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62774-y",
  "id": "0w52fd6cdev42",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62774-y",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2204|Biosensors and Analytical Detection"
    ]
  ],
  "openalex": "W7170156151",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62774-y_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/0w52fd6cdev42.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:52Z",
      "native_id": "2774457739b34d4a9af09da9cd07ff0f",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62774-y"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Blood drop patterns and explainable AI reveal livestock anemia signatures",
  "title_text": "Blood drop patterns and explainable AI reveal livestock anemia signatures",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:52Z",
  "url": "https://research.draconitic.com/paper/0w52fd6cdev42",
  "venue": "Scientific Reports",
  "versions": []
}