Advances and Challenges in the Diagnosis of Vector-Borne Protozoal Infections in Veterinary Medicine
Abstract
1. Introduction
2. Microscopic Diagnosis
2.1. Blood Smear Examination
2.2. Tissue-Stage Parasites and Exo-Erythrocytic Development
2.3. Advances in Artificial Intelligence for Diagnosis of Parasitoses in Clinical Samples
3. Serological Diagnosis of Vector-Borne Protozoal Infections
3.1. The Role of Serology in Vector-Borne Protozoal Diagnosis
3.2. Serology in Clinical Practice
3.3. Serology for Herd Management and Surveillance
3.4. Interpreting a Serological Result: Four Limitations
3.5. Implications for Surveillance in Vaccinated Populations
4. Molecular Diagnosis
4.1. Target Selection

4.2. Amplification Methodologies
4.3. Commercially Available Assays
5. Surveillance of Vector-Borne Protozoal Infections: Challenges and a One Health Perspective
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
| AI | artificial intelligence |
| BYOL | bootstrap your own latent (self-supervised learning method) |
| CATT | card agglutination test for trypanosomiasis |
| CBIR | content-based image retrieval |
| CE-IVD | Conformité Européenne—In Vitro Diagnostic |
| cox3 | cytochrome c oxidase subunit III gene |
| CRISPR | clustered regularly interspaced short palindromic repeats |
| ddPCR | droplet digital PCR |
| DAT | direct agglutination test |
| DIVA | Differentiating Infected from Vaccinated Animals |
| DNA | deoxyribonucleic acid |
| DTU | discrete typing unit |
| ELISA | enzyme-linked immunosorbent assay |
| EMPRES-i+ | Emergency Prevention System for Animal Health Global Information System |
| ERA-Cas12a | extension of recombinase-aided amplification coupled with CRISPR-Cas12a detection |
| FAO | Food and Agriculture Organization of the United Nations |
| FDA | U.S. Food and Drug Administration |
| FIND | Foundation for Innovative New Diagnostics |
| HAT | human African trypanosomiasis |
| IFAT | indirect fluorescence antibody test |
| ITS | internal transcribed spacer |
| kDNA | kinetoplast DNA |
| KNN | k-nearest neighbours |
| LAMP | loop-mediated isothermal amplification |
| LDT | laboratory-developed test |
| mAECT | miniature anion-exchange centrifugation technique |
| ML | machine learning |
| MPSP | major piroplasm surface protein |
| mRNA | messenger RNA |
| nad5 | NADH dehydrogenase subunit 5 gene |
| NCBI | National Center for Biotechnology Information |
| nMLST | nuclear multilocus sequence typing |
| nUPDx | nested universal parasite diagnostic assay |
| PAAT | Programme Against African Trypanosomosis |
| PAHO | Pan American Health Organization |
| PCR | polymerase chain reaction |
| POC | point of care |
| QBC | quantitative buffy coat |
| qPCR | quantitative PCR |
| rDNA | ribosomal DNA |
| RDT | rapid diagnostic test |
| RNA | ribonucleic acid |
| RUO | Research Use Only |
| RPA | recombinase polymerase amplification |
| rRNA | ribosomal RNA |
| SAR | Stramenopiles–Alveolates–Rhizaria (a major eukaryotic supergroup) |
| SSL | self-supervised learning |
| SSU | small subunit (ribosomal RNA) |
| SYBR | SYBR Green intercalating fluorescent dye |
| VEuMAP | VEuPathDB map (eukaryotic pathogen epidemiological and population-level data platform) |
| WAHIS | World Animal Health Information System |
| WHO | World Health Organization |
| WOAH | World Organisation for Animal Health |
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| Host | Parasite(s) | AI Method * | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) | Reference | Dataset Size ** |
|---|---|---|---|---|---|---|---|---|
| Dog | Babesia spp. | WeightedEnsemble | 97.75 | 98 | 97.5 | 98 | [36] | 4000 |
| Dog | Babesia gibsoni | SimCLR (EfficientNet_b2) | 97.09 | 94.5 | 97.3 | 95.9 | [37] | 1578 |
| Horse | Theileria equi, Babesia caballi | YOLOv8 ‡ | 91 | 98 | 92 | 95 | [38] ‡ | 2000 |
| Poultry | Plasmodium gallinaceum | YOLOv3 + Darknet | 99.2 | — § | 99.2 | — | [39] | 12,761 |
| Mice | Trypanosoma cruzi | ML—Random Forest | 89.5 | 87.6 | — | 89 | [40] | 2628 |
| Human/Animal (archived) | Trypanosoma brucei, T. cruzi, Trypanosoma evansi | Deep Metric Learning (ResNet50 + CBIR/KNN) | 99.71 | 93.5 | 96.6 | 94.9 | [41] | 32,276 |
| Multiple (archived) | Babesia spp., Leishmania spp., Plasmodium spp., Trypanosoma spp. | BYOL SSL (ResNet50) | 99.2 | 98.9 | 98.2 | 98.7 | [42] | 33,694 |
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Cevallos, A.M.; Meraz-Tay, T.; Hernández, R. Advances and Challenges in the Diagnosis of Vector-Borne Protozoal Infections in Veterinary Medicine. Pathogens 2026, 15, 561. https://doi.org/10.3390/pathogens15060561
Cevallos AM, Meraz-Tay T, Hernández R. Advances and Challenges in the Diagnosis of Vector-Borne Protozoal Infections in Veterinary Medicine. Pathogens. 2026; 15(6):561. https://doi.org/10.3390/pathogens15060561
Chicago/Turabian StyleCevallos, Ana María, Tomas Meraz-Tay, and Roberto Hernández. 2026. "Advances and Challenges in the Diagnosis of Vector-Borne Protozoal Infections in Veterinary Medicine" Pathogens 15, no. 6: 561. https://doi.org/10.3390/pathogens15060561
APA StyleCevallos, A. M., Meraz-Tay, T., & Hernández, R. (2026). Advances and Challenges in the Diagnosis of Vector-Borne Protozoal Infections in Veterinary Medicine. Pathogens, 15(6), 561. https://doi.org/10.3390/pathogens15060561

