Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers

Autores
Reineri, Pablo Sebastian; Roldan Bernhard, Sergio Daniel; Principi, Santiago Alberto; Aller Atucha, Juan Florencio
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This article aims to develop a predictive model of gestational status nineteen days after artificial insemination, using color ultrasonography and machine learning strategies in beef heifers. One hundred heifers were included in the study and on day 0 of the experiment underwent fixed-time artificial insemination. In addition, live weight, body condition score, uterine diameter, estradiol and progesterone concentration were determined. On day nineteen, the presence and area of the corpus luteum, area of the cavity of the corpus luteum, estradiol and progesterone concentrations, and vascularized area of the corpus luteum and blood flow of the corpus luteum were determined. Pregnancy diagnosis was made on days 35. To identify the model predictive capacity, we implemented a machine learning strategy, specifically a Random Forest Classifier. Two models were evaluated, the complete and reduced models. Model performances were quantified with the accuracy, specificity, sensitivity, precision, error and area under the receiver operating characteristic curve (AUC). Complete model produced accuracy of 75.3%, sensitivity of 82.5%, specificity of 66.6%, precision of 75%, error of 24.6% and AUC of 0.97. Reduced Model produced accuracy of 78%, sensitivity 87.5%, specificity 66.6%, precision 76%, error of 21.9% and AUC of 0.95. In conclusion, the developed models reasonably predicted gestational status. While these findings should be interpreted as a tool under development and require increased sample size and external validation to confirm these preliminary observations, they offer an initial perspective on the importance of using machine learning strategies that integrate reproductive biotechnology, precision livestock farming, and computational analysis applied to bovine fertility.
EEA Santiago del Estero
Fil: Reineri, Pablo Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina.
Fil: Reineri, Pablo Sebastián. Universidad Nacional de Santiago del Estero. Facultad de Agronomía y Agroindustrias; Argentina
Fil: Roldan Bernhard, Sergio Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina.
Fil: Principi, Santiago Alberto. Actividad privada; Argentina
Fil: Aller Atucha, Juan Florencio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
Fuente
Tropical Animal Health and Production 58 (2) : article number 162. (March 2026)
Materia
Ganado Bovino
Ganado de Carne
Reproducción Animal
Gestación
Inseminación Artificial
Ultrasonografía
Aprendizaje Automático
Cattle
Beef Cattle
Animal Reproduction
Pregnancy
Artificial Insemination
Ultrasonography
Machine Learning
Corpus Luteum
Cuerpo Lúteo
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/27317
Acceder
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oai_identifier_str oai:localhost:20.500.12123/27317
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifersReineri, Pablo SebastianRoldan Bernhard, Sergio DanielPrincipi, Santiago AlbertoAller Atucha, Juan FlorencioGanado BovinoGanado de CarneReproducción AnimalGestaciónInseminación ArtificialUltrasonografíaAprendizaje AutomáticoCattleBeef CattleAnimal ReproductionPregnancyArtificial InseminationUltrasonographyMachine LearningCorpus LuteumCuerpo LúteoThis article aims to develop a predictive model of gestational status nineteen days after artificial insemination, using color ultrasonography and machine learning strategies in beef heifers. One hundred heifers were included in the study and on day 0 of the experiment underwent fixed-time artificial insemination. In addition, live weight, body condition score, uterine diameter, estradiol and progesterone concentration were determined. On day nineteen, the presence and area of the corpus luteum, area of the cavity of the corpus luteum, estradiol and progesterone concentrations, and vascularized area of the corpus luteum and blood flow of the corpus luteum were determined. Pregnancy diagnosis was made on days 35. To identify the model predictive capacity, we implemented a machine learning strategy, specifically a Random Forest Classifier. Two models were evaluated, the complete and reduced models. Model performances were quantified with the accuracy, specificity, sensitivity, precision, error and area under the receiver operating characteristic curve (AUC). Complete model produced accuracy of 75.3%, sensitivity of 82.5%, specificity of 66.6%, precision of 75%, error of 24.6% and AUC of 0.97. Reduced Model produced accuracy of 78%, sensitivity 87.5%, specificity 66.6%, precision 76%, error of 21.9% and AUC of 0.95. In conclusion, the developed models reasonably predicted gestational status. While these findings should be interpreted as a tool under development and require increased sample size and external validation to confirm these preliminary observations, they offer an initial perspective on the importance of using machine learning strategies that integrate reproductive biotechnology, precision livestock farming, and computational analysis applied to bovine fertility.EEA Santiago del EsteroFil: Reineri, Pablo Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina.Fil: Reineri, Pablo Sebastián. Universidad Nacional de Santiago del Estero. Facultad de Agronomía y Agroindustrias; ArgentinaFil: Roldan Bernhard, Sergio Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina.Fil: Principi, Santiago Alberto. Actividad privada; ArgentinaFil: Aller Atucha, Juan Florencio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaSpringer2026-08-06T12:39:12Z2026-08-06T12:39:12Z2026-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/27317https://link.springer.com/article/10.1007/s11250-026-04964-40049-47471573-7438https://doi.org/10.1007/s11250-026-04964-4Tropical Animal Health and Production 58 (2) : article number 162. (March 2026)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2023-PD-L01-I112, Biotecnologías reproductivas y plataforma de edición génica para animales de interés zootécnico.info:eu-repograntAgreement/INTA/2023-PE-L01-I057, Desarrollo de herramientas y estrategias para una ganadería sostenible en la región NOAinfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-09-24T11:43:39Zoai:localhost:20.500.12123/27317instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-09-24 11:43:40.507INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers
title Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers
spellingShingle Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers
Reineri, Pablo Sebastian
Ganado Bovino
Ganado de Carne
Reproducción Animal
Gestación
Inseminación Artificial
Ultrasonografía
Aprendizaje Automático
Cattle
Beef Cattle
Animal Reproduction
Pregnancy
Artificial Insemination
Ultrasonography
Machine Learning
Corpus Luteum
Cuerpo Lúteo
title_short Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers
title_full Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers
title_fullStr Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers
title_full_unstemmed Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers
title_sort Prediction of gestational status nineteen days after artificial insemination using color ultrasonography and machine learning strategies in beef heifers
dc.creator.none.fl_str_mv Reineri, Pablo Sebastian
Roldan Bernhard, Sergio Daniel
Principi, Santiago Alberto
Aller Atucha, Juan Florencio
author Reineri, Pablo Sebastian
author_facet Reineri, Pablo Sebastian
Roldan Bernhard, Sergio Daniel
Principi, Santiago Alberto
Aller Atucha, Juan Florencio
author_role author
author2 Roldan Bernhard, Sergio Daniel
Principi, Santiago Alberto
Aller Atucha, Juan Florencio
author2_role author
author
author
dc.subject.none.fl_str_mv Ganado Bovino
Ganado de Carne
Reproducción Animal
Gestación
Inseminación Artificial
Ultrasonografía
Aprendizaje Automático
Cattle
Beef Cattle
Animal Reproduction
Pregnancy
Artificial Insemination
Ultrasonography
Machine Learning
Corpus Luteum
Cuerpo Lúteo
topic Ganado Bovino
Ganado de Carne
Reproducción Animal
Gestación
Inseminación Artificial
Ultrasonografía
Aprendizaje Automático
Cattle
Beef Cattle
Animal Reproduction
Pregnancy
Artificial Insemination
Ultrasonography
Machine Learning
Corpus Luteum
Cuerpo Lúteo
dc.description.none.fl_txt_mv This article aims to develop a predictive model of gestational status nineteen days after artificial insemination, using color ultrasonography and machine learning strategies in beef heifers. One hundred heifers were included in the study and on day 0 of the experiment underwent fixed-time artificial insemination. In addition, live weight, body condition score, uterine diameter, estradiol and progesterone concentration were determined. On day nineteen, the presence and area of the corpus luteum, area of the cavity of the corpus luteum, estradiol and progesterone concentrations, and vascularized area of the corpus luteum and blood flow of the corpus luteum were determined. Pregnancy diagnosis was made on days 35. To identify the model predictive capacity, we implemented a machine learning strategy, specifically a Random Forest Classifier. Two models were evaluated, the complete and reduced models. Model performances were quantified with the accuracy, specificity, sensitivity, precision, error and area under the receiver operating characteristic curve (AUC). Complete model produced accuracy of 75.3%, sensitivity of 82.5%, specificity of 66.6%, precision of 75%, error of 24.6% and AUC of 0.97. Reduced Model produced accuracy of 78%, sensitivity 87.5%, specificity 66.6%, precision 76%, error of 21.9% and AUC of 0.95. In conclusion, the developed models reasonably predicted gestational status. While these findings should be interpreted as a tool under development and require increased sample size and external validation to confirm these preliminary observations, they offer an initial perspective on the importance of using machine learning strategies that integrate reproductive biotechnology, precision livestock farming, and computational analysis applied to bovine fertility.
EEA Santiago del Estero
Fil: Reineri, Pablo Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina.
Fil: Reineri, Pablo Sebastián. Universidad Nacional de Santiago del Estero. Facultad de Agronomía y Agroindustrias; Argentina
Fil: Roldan Bernhard, Sergio Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina.
Fil: Principi, Santiago Alberto. Actividad privada; Argentina
Fil: Aller Atucha, Juan Florencio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
description This article aims to develop a predictive model of gestational status nineteen days after artificial insemination, using color ultrasonography and machine learning strategies in beef heifers. One hundred heifers were included in the study and on day 0 of the experiment underwent fixed-time artificial insemination. In addition, live weight, body condition score, uterine diameter, estradiol and progesterone concentration were determined. On day nineteen, the presence and area of the corpus luteum, area of the cavity of the corpus luteum, estradiol and progesterone concentrations, and vascularized area of the corpus luteum and blood flow of the corpus luteum were determined. Pregnancy diagnosis was made on days 35. To identify the model predictive capacity, we implemented a machine learning strategy, specifically a Random Forest Classifier. Two models were evaluated, the complete and reduced models. Model performances were quantified with the accuracy, specificity, sensitivity, precision, error and area under the receiver operating characteristic curve (AUC). Complete model produced accuracy of 75.3%, sensitivity of 82.5%, specificity of 66.6%, precision of 75%, error of 24.6% and AUC of 0.97. Reduced Model produced accuracy of 78%, sensitivity 87.5%, specificity 66.6%, precision 76%, error of 21.9% and AUC of 0.95. In conclusion, the developed models reasonably predicted gestational status. While these findings should be interpreted as a tool under development and require increased sample size and external validation to confirm these preliminary observations, they offer an initial perspective on the importance of using machine learning strategies that integrate reproductive biotechnology, precision livestock farming, and computational analysis applied to bovine fertility.
publishDate 2026
dc.date.none.fl_str_mv 2026-08-06T12:39:12Z
2026-08-06T12:39:12Z
2026-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/27317
https://link.springer.com/article/10.1007/s11250-026-04964-4
0049-4747
1573-7438
https://doi.org/10.1007/s11250-026-04964-4
url http://hdl.handle.net/20.500.12123/27317
https://link.springer.com/article/10.1007/s11250-026-04964-4
https://doi.org/10.1007/s11250-026-04964-4
identifier_str_mv 0049-4747
1573-7438
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2023-PD-L01-I112, Biotecnologías reproductivas y plataforma de edición génica para animales de interés zootécnico.
info:eu-repograntAgreement/INTA/2023-PE-L01-I057, Desarrollo de herramientas y estrategias para una ganadería sostenible en la región NOA
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Tropical Animal Health and Production 58 (2) : article number 162. (March 2026)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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