Deep learning for diagnostic and clinical decision support in companion animals: a systematic review of CNN- and LSTM-based approaches
- 1Facultad de Ingeniería en Sistemas e Informática, Universidad Tecnológica del Perú, Lima, Peru
- 2Centro de Apoyo Logístico al Investigador (CALI), Universidad Tecnológica del Perú, Lima, Peru
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.
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BibTeX
@article{guevaraflores2026deep,
title = {{Deep learning for diagnostic and clinical decision support in companion animals: a systematic review of CNN- and LSTM-based approaches}},
author = {Juan J. Guevara-Flores and Jhonatan López-Rojas and José A. Vásquez–Coronel},
journal = {Frontiers in Veterinary Science},
year = {2026},
month = oct,
doi = {10.3389/fvets.2026.1952589},
url = {https://doi.org/10.3389/fvets.2026.1952589},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Deep learning for diagnostic and clinical decision support in companion animals: a systematic review of CNN- and LSTM-based approaches
AU - Juan J. Guevara-Flores
AU - Jhonatan López-Rojas
AU - José A. Vásquez–Coronel
PY - 2026
DA - 2026/10/22
JO - Frontiers in Veterinary Science
DO - 10.3389/fvets.2026.1952589
UR - https://doi.org/10.3389/fvets.2026.1952589
AB - 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.
SN - 2297-1769
ER -