Machine learning for determining the stage of the estrous cycle in bitches

Authors

  • João Vítor Gonçalves de Oliveira Centro Universitário do Leste de Minas
  • Gabrielly Pereira Ribeiro
  • Domink Lenz
  • Ricardo Lopes Dias da Costa
  • Célio Siman Mafra Nunes
  • Mayra Cunha Flecher
  • Renato Travassos Beltrame

DOI:

https://doi.org/10.5380/avs.v29i2.94953

Keywords:

CellProfiller, cytology, reproduction

Abstract

Reproductive biotechnologies, such as artificial insemination, are important tools in the reproduction of female dogs. Accurate determination of the specific stage of the estrous cycle is crucial for the successful application of these technologies.Vaginal cytology serves as a cost-effective and rapid diagnostic solution. However, itrelies on the analyzer’s expertise, making itsubject to human errors. Additionally, it may involve a prolonged duration between sample collection and result analysis.To minimize these limitations and streamline the diagnostic process,this studydeveloped software to automate the identification of the main phases of the estrous cycle that are important for artificial insemination. Eighteen vaginal cytology images were used, withsix images representing each of the phases studied (proestrus, estrus, and diestrus).Imageswere analyzed using the open source CellProfiller software, with subsequentclassification of the images using theTanagra software. Sensitivity and specificity valueswere determined for the proestrus, estrus, and diestrus phases, yielding results of 0.99, 0.86, 0.95, and 1, 0.95, 0.82, respectively.These findingsdemonstrate the model’scapacityfor correctly identifyingdifferent phases of the estrous cycle. The proposed model proved effective for the study’sobjective, and the authors suggest that it may be applicable to other economically important species. 

References

Alm, H., & Holst, B. S. (2018). Identifying ovarian tissue in the bitch using anti-Müllerian hormone (AMH) or luteinizing hormone (LH). Theriogenol., 106, 15-20. https://doi.org/10.1016/j.theriogenology.2017.09.028.

Awaysheh, A., Wilcke, J., Elvinger, F., Rees, L., Fan, W., & Zimmerman, K. L. (2019). Review of medical decision support and machine-learning methods. Vete path., 56(4), 512-525. https://doi.org/10.1177/0300985819829524.

Bittencourt, R. F., de Melo Santos, M., de Jesus, E. O., de Matos, B. A. P., Barreto, W. M., & Chalhoub, M. (2014). Eficácia da inseminação vaginal profunda em cadelas monitoradas por citologia vaginal. Rev. de Educ. Cont. em Med. Vet. e Zootec. do CRMV-SP, 12(3), 60-60.

Calderón G, Carrillo C, Nakano-Miyatake M, J.C Conde Acevedo, José Ernesto Hernández. Automatic Estrus Cycle Identification System on Female Dogs Based on Deep Learning. Lec. Not. in Comp. Sci. 2020;12088. https://doi.org/10.1007/978-3-030-49076-8_25.

Cihan, P., Gökçe, E., & Kalıpsız, O. (2017). A review of machine learning applications in veterinary field. https://doi.org/10.9775/kvfd.2016.17281.

Curti, P. D. F., Selli, A., Pinto, D. L., Merlos-Ruiz, A., Balieiro, J. C. D. C., & Ventura, R. V. (2023). Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview. Anim. Repro., 20, e20230077. https://doi.org/10.1590/1984-3143-ar2023-0077.

Gaytán, L., Rascón, C. R., Angel-García, O., Véliz, F. G., Contreras, V., & Mellado, M. (2020). Factors influencing English Bulldog bitch fertility after surgical uterine deposition of fresh semen. Theriogenol., 142, 315-319. https://doi.org/10.1016/j.theriogenology.2019.10.018.

GOERICKE‐PESCH, S. Long‐term effects of GnRH agonists on fertility and behaviour. Repro. in domest. animals, v. 52, p. 336-347, 2017. https://doi.org/10.1111/rda.12898.

Hernández Hernández, G., Delgado Toral, L., Ochoa Montiel, M. D. R., Zamora Gómez, E., Sossa, H., Barreto Flores, A., ... & Reyes Luna, R. (2019). Estrous cycle classification through automatic feature extraction. Comput. y Sist., 23(4), 1249-1259. https://doi.org/10.13053/cys-23-4-3095.

Hooper, S. E., Hecker, K. G., & Artemiou, E. (2023). Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators. Vet. Sci., 10(9), 537. https://doi.org/10.3390/vetsci10090537.

Laghi, V., Ricci, V., De Santa, F., & Torcinaro, A. (2022). A user-friendly approach for routine histopathological and morphometric analysis of skeletal muscle using cellprofiler software. Diagnost., 12(3), 561. https://doi.org/10.3390/diagnostics12030561.

Lau, Y. S., Xu, L., Gao, Y., & Han, R. (2018). Automated muscle histopathology analysis using CellProfiler. Skel. Muscl., 8, 1-9. https://doi.org/10.1186/s13395-018-0178-6.

Lindh, L., Kowalewski, M. P., Günzel-Apel, A. R., Goericke-Pesch, S., Myllys, V., Schuler, G., ... & Peltoniemi, O. A. (2022). Ovarian and uterine changes during the oestrous cycle in female dogs. Reprod., Fert. and Dev., 35(4), 321-337. https://doi.org/10.1071/RD22177.

Mason, S. J. (2018). Current review of artificial insemination in dogs. Vet. Clin.: Small Animal Practice, 48(4), 567-580. https://doi.org/10.1016/j.cvsm.2018.02.005.

Moscon, L. M., Macedo, N. D., Nunes, C. S. M., Boasquevisque, P. C. R., de Andrade, T. U., Endringer, D. C., & Lenz, D. (2019). Automated detection of anomalies in cervix cells using image analysis and machine learning. Comp. Clin. Path., 28, 177-182. https://doi.org/10.1007/s00580-018-2812-4.

Oliveira, G. P., de Souza, H. F. F., Batista, D. P., Silva, A., da Silva, W. C., & Silva, L. K. X. (2021). Emprego da citologia vaginal na detecção da fase do ciclo estral de cadelas e sua relação com a idade e escore de condição corporal, Belém, Pará. Res., Soc. and Dev., 10(9), e25310917921-e25310917921. https://doi.org/10.33448/rsd-v10i9.17921.

PanóticoRápido: - Laborclin | Grupo Solabia 2019. https://www.laborclin.com.br/panotico-rapido (accessed October 7, 2023).

Pimentel, M. M. L., dos Santos, F. A., da Cunha Dias, R. V., de Macêdo, L. B., de Souza Fonseca, Z. A. A., André, W. P. P., & Ribeiro, W. L. C. (2014). Monitoramento do ciclo estral de fêmeas equinas por meio de citologia vaginal, ultrassonografia e dosagem hormonal. Arq. de Ciên. Vet. e Zool. da UNIPAR, 17(1). https://doi.org/10.25110/arqvet.v17i1.4920.

Porto, R. R. M., Cavalcante, T. V., Dias, F. E. F., do Nascimento Rocha, J. M., & de Souza, J. A. T. (2007). Perfil citológico vaginal de ovelhas da raça Santa Inês no acompanhamento do ciclo estral. Brazil. Ani. Sci., 8(3), 521-528. https://doi.org/10.5216/cab.v8i3.1729.

Reckers, F., Klopfleisch, R., Belik, V., & Arlt, S. (2022). Canine vaginal cytology: a revised definition of exfoliated vaginal cells. Front. in Vet. Sci., 9, 834031. https://doi.org/10.3389/fvets.2022.834031.

Romagnoli, S. (2017). Top 5 reproduction concerns in dogs.

Sano, K., Matsuda, S., Tohyama, S., Komura, D., Shimizu, E., & Sutoh, C. (2020). Deep learning-based classification of the mouse estrous cycle stages. Sci. report., 10(1), 11714. https://doi.org/10.1038/s41598-020-68611-0.

Sharma, M., & Sharma, N. (2016). Vaginal cytology: an historical perspective on its diagnostic use. Adv. Anim. Vet. Sci, 4(6), 283-288. https://doi.org/10.14737/journal.aavs/2016/4.6.283.288.

Teodoro, J. V. S, Zuffo, F. C., de Souza, W. J., de Almeida Rabello, D., Nunes, G. D., Venâncio, D. C., ... & de Abreu, D. A. C. (2023). Avaliação da citologia vaginal do ciclo estral fisiológico de um grupo de fêmeas bovinas comparado a um grupo de fêmeas com ciclo estral induzido por um protocolo de IATF. Braz. Jour. of Ani. and Envi. Res., 6(2), 1883-1888. https://doi.org/10.34188/bjaerv6n2-074.

Turmalay L, Duro S, Lika E, Ceroni V. The hormonal control of estrus in bitches. Journ. of Ani. and Vet. Adv. 2011;10:2447-2449.

Vermeirsch, H., Van den Broeck, W., Coryn, M., & Simoens, P. (2002). Immunohistochemical detection of androgen receptors in the canine uterus throughout the estrus cycle. Theriogenology, 57(9), 2203-2216. https://doi.org/10.1016/s0093-691x(02)00908-1.

Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005, June). Practical machine learning tools and techniques. In Data mining (Vol. 2, No. 4, pp. 403-413). Amsterdam, The Netherlands: Elsevier. ISSN : 0922-3444

Wolcott, N. S., Sit, K. K., Raimondi, G., Hodges, T., Shansky, R. M., Galea, L. A., ... & Goard, M. J. (2022). Automated classification of estrous stage in rodents using deep learning. Sci. Report., 12(1), 17685. https://doi.org/10.1038/s41598-022-22392-w.

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Published

2024-06-14

How to Cite

Oliveira, J. V. G. de, Ribeiro, G. P., Lenz, D., Costa, R. L. D. da, Nunes, C. S. M., Flecher, M. C., & Beltrame, R. T. (2024). Machine learning for determining the stage of the estrous cycle in bitches. Archives of Veterinary Science, 29(2). https://doi.org/10.5380/avs.v29i2.94953

Issue

Vol. 29 No. 2 (2024)

Section

Animal Reproduction

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