{
  "abstract": "Early disease detection prior to the onset of advanced clinical signs remains a critical imperative in veterinary medicine. This systematic review provides a methodological synthesis of Convolutional Neural Networks and Long Short-Term Memory networks applied to diagnostic and clinical decision support in canine and feline patients, evaluating both algorithmic performance and clinical feasibility. Adhering to the PICO-CS framework and PRISMA 2020 guidelines, we queried Scopus and Web of Science for peer-reviewed studies published between 2021 and 2026, retrieving a final corpus of 57 primary studies. Eleven clinical domains were identified, with evidence heavily concentrated in Oncology (28.1%), Cardiology (15.8%), and Pulmonology (12.3%). CNN architectures predominated in spatial imaging modalities—including radiographs, clinical photography, ultrasonography, and microscopic imagery—whereas LSTM networks were deployed for temporal signals and sensor-derived telemetry. Binary classification tasks achieved the highest performance metrics (accuracy reaching up to 99%), whereas multi-class formulations exhibited broader variability due to dataset imbalance. Cross-study benchmarking revealed substantial heterogeneity in datasets, evaluation metrics, and validation protocols, while reliance on high-performance computational infrastructure poses a barrier to point-of-care deployment in resource-constrained clinics. We conclude that while deep learning architectures hold significant promise as clinical decision-support tools, transitioning to routine veterinary practice requires prospective external validation, representative open-access datasets, and lightweight models optimized for conventional clinical hardware.",
  "abstract_shown": true,
  "affiliations": [
    "Facultad de Ingeniería en Sistemas e Informática, Universidad Tecnológica del Perú, Lima, Peru",
    "Centro de Apoyo Logístico al Investigador (CALI), Universidad Tecnológica del Perú, Lima, Peru"
  ],
  "article_type": "systematic review",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Juan J. Guevara-Flores"
    },
    {
      "affs": [
        1
      ],
      "name": "Jhonatan López-Rojas"
    },
    {
      "affs": [
        1
      ],
      "name": "José A. Vásquez–Coronel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7vyyaqszxzwcj.bib",
  "cited_by": 0,
  "doi": "10.3389/fvets.2026.1952589",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736258/",
  "id": "7vyyaqszxzwcj",
  "issns": [
    "2297-1769"
  ],
  "landing_url": "https://doi.org/10.3389/fvets.2026.1952589",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Systematic Review"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223187469",
  "pdf_url": "https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2026.1952589/pdf",
  "pmcid": "PMC13736258",
  "pmid": "42991619",
  "published": "2026-10-22",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/7vyyaqszxzwcj.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:21:45Z",
      "native_id": "PMC13736258",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fvets.2026.1952589"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Deep learning for diagnostic and clinical decision support in companion animals: a systematic review of CNN- and LSTM-based approaches",
  "title_text": "Deep learning for diagnostic and clinical decision support in companion animals: a systematic review of CNN- and LSTM-based approaches",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:21:45Z",
  "url": "https://research.draconitic.com/paper/7vyyaqszxzwcj",
  "venue": "Frontiers in Veterinary Science",
  "versions": []
}