Machine learning for determining the stage of the estrous cycle in bitches - Oliveira, João Vítor Gonçalves de; Ribeiro, Gabrielly Pereira; Lenz, Domink; Costa, Ricardo Lopes Dias da; Nunes, Célio Siman Mafra; Flecher, Mayra Cunha; Beltrame, Renato Travassos (Archives of Veterinary Science, 2024)
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.