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Campanholi SP, Garcia Neto S, Pinheiro GM, Nogueira MFG, Rocha JC, Losano JDDA, Siqueira AFP, Nichi M, Assumpção MEOD, Basso AC, Monteiro FM, Gimenes LU. Can in vitro embryo production be estimated from semen variables in Senepol breed by using artificial intelligence? Front Vet Sci 2023; 10:1254940. [PMID: 37808114 PMCID: PMC10551135 DOI: 10.3389/fvets.2023.1254940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/31/2023] [Indexed: 10/10/2023] Open
Abstract
Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as in vitro embryo production (IVEP) in cattle, could be valuable for livestock production. This study was developed to model IVEP estimates in Senepol animals based on various sperm attributes, through retrospective data from 290 IVEP routines performed using 38 commercial doses of semen from Senepol bulls. All sperm samples that had undergone the same procedure during sperm selection for in vitro fertilization were evaluated using a computer-assisted sperm analysis (CASA) system to define sperm subpopulations. Sperm morphology was also analyzed in a wet preparation, and the integrity of the plasma and acrosomal membranes, mitochondrial potential, oxidative status, and chromatin resistance were evaluated using flow cytometry. A previous study identified three sperm subpopulations in such samples and the information used in tandem with other sperm quality variables to perform an AI analysis. AI analysis generated models that estimated IVEP based on the season, donor, percentage of viable oocytes, and 18 other sperm predictor variables. The accuracy of the results obtained for the three best AI models for predicting the IVEP was 90.7, 75.3, and 79.6%, respectively. Therefore, applying this AI technique would enable the estimation of high or low embryo production for individual bulls based on the sperm analysis information.
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Affiliation(s)
- Suzane Peres Campanholi
- Departamento de Patologia, Reprodução e Saúde Única, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista, Jaboticabal, Brazil
| | | | - Gabriel Martins Pinheiro
- Departamento de Ciências Biológicas, Faculdade de Ciências e Letras (FCLA), Universidade Estadual Paulista (UNESP), Assis, Brazil
| | - Marcelo Fábio Gouveia Nogueira
- Departamento de Ciências Biológicas, Faculdade de Ciências e Letras (FCLA), Universidade Estadual Paulista (UNESP), Assis, Brazil
| | - José Celso Rocha
- Departamento de Ciências Biológicas, Faculdade de Ciências e Letras (FCLA), Universidade Estadual Paulista (UNESP), Assis, Brazil
| | - João Diego de Agostini Losano
- Departamento de Reprodução Animal, Faculdade de Medicina Veterinária e Zootecnia (FMVZ), Universidade de São Paulo (USP), São Paulo, Brazil
| | - Adriano Felipe Perez Siqueira
- Departamento de Reprodução Animal, Faculdade de Medicina Veterinária e Zootecnia (FMVZ), Universidade de São Paulo (USP), São Paulo, Brazil
| | - Marcílio Nichi
- Departamento de Reprodução Animal, Faculdade de Medicina Veterinária e Zootecnia (FMVZ), Universidade de São Paulo (USP), São Paulo, Brazil
| | | | | | - Fabio Morato Monteiro
- Centro Avançado de Pesquisa de Bovinos de Corte, Agência Paulista de Tecnologia dos Agronegócios/Instituto de Zootecnia (APTA/IZ), Sertãozinho, Brazil
| | - Lindsay Unno Gimenes
- Departamento de Patologia, Reprodução e Saúde Única, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista, Jaboticabal, Brazil
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Rahman M, Mandal A, Gayari I, Bidyalaxmi K, Sarkar D, Allu T, Debbarma A. Prospect and scope of artificial neural network in livestock farming: a review. BIOL RHYTHM RES 2022. [DOI: 10.1080/09291016.2022.2139389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Mokidur Rahman
- Animal Genetics and Breeding, Eastern Regional Station, ICAR-NDRI, Kalyani, India, 741235
| | - Ajoy Mandal
- Animal Genetics and Breeding, Eastern Regional Station, ICAR-NDRI, Kalyani, India, 741235
| | - Indrajit Gayari
- Animal Genetics and Breeding, Eastern Regional Station, ICAR-NDRI, Kalyani, India, 741235
| | - Kangabam Bidyalaxmi
- Animal Genetics and Breeding, Eastern Regional Station, ICAR-NDRI, Kalyani, India, 741235
| | - Debajyoti Sarkar
- Animal Reproduction Gynaecology and Obstetrics, Eastern Regional Station, ICAR- NDRI, Kalyani, India, 741235
| | - Teja Allu
- Animal Reproduction Gynaecology and Obstetrics, Southern Regional Station, ICAR-NDRI, Adugodi, India, 560030
| | - Asish Debbarma
- Livestock Production and Management, Eastern Regional Station, ICAR-NDRI, Kalyani, India, 741235
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Alyethodi RR, Deb R, Alex R, Kumar S, Singh U, Tyagi S, Mandal D, Raja T, Das A, Sharma S, Sengar GS, Singh R, Prakash B. Molecular markers, BM1500 and UMN2008, are associated with post-thaw motility of bull sperm. Anim Reprod Sci 2016; 174:143-149. [DOI: 10.1016/j.anireprosci.2016.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 10/04/2016] [Accepted: 10/07/2016] [Indexed: 02/07/2023]
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