Bibliographic Details
| Title: |
Machine learning for determining the stage of the estrous cycle in bitches: a preliminary data collection. |
| Authors: |
de Oliveira, João Vítor Gonçalves, Ribeiro, Gabrielly Pereira, Lenz, Domink, da Costa, Ricardo Lopes Dias, Nunes, Célio Siman Mafra, Flecher, Mayra Cunha, Beltrame, Renato Travassos |
| Source: |
Archives of Veterinary Science; 2024, Vol. 29 Issue 2, p1-16, 16p |
| Subject Terms: |
ESTRUS, MACHINE learning, IMAGE recognition (Computer vision), FEMALE dogs, ACQUISITION of data, ARTIFICIAL insemination, IDENTIFICATION |
| 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, it relies on the analyzer's expertise, subjecting it 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 study has developed and tested software in Pyphon language based on windows platform to automate the identification of the main phases of the estrous cycle that are important for artificial insemination. Eighteen vaginal cytology images were used, with six images representing each of the phases studied (proestrus, estrus, and diestrus). Images were analyzed using the open-source CellProfiller software, with subsequent classification of the images using the Tanagra software. Sensitivity and specificity values were determined for the proestrus, estrus, and diestrus phases, yielding results of 0.99 and 0.86 sensitivity and specificity for proestrus, 0.95, and 1 sensitivity and specificity for estrus and 0.95, 0.82 sensitivity and specificity for diestrus. These findings suggest that the model's capacity to correctly identify different phases of the estrous cycle. The proposed model proved effective for the study's objective, and the authors suggest that it may could be applied to other economically important species such as cattle, horses and small ruminants. [ABSTRACT FROM AUTHOR] |
|
Copyright of Archives of Veterinary Science is the property of Archives of Veterinary Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |
