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EMBRİYO ÜRETİMİNDE YAPAY ZEKÂ KULLANIMI

Yıl 2024, Cilt: 15 Sayı: 3, 150 - 158, 31.12.2024
https://doi.org/10.38137/vftd.1522154

Öz

İnsan ve hayvan embriyo üretim aşamalarında 2000’li yıllardan itibaren rol almaya başlayan yapay zekâ, son yıllarda yapılan umut verici çalışmalarla en gözde konulardan biridir. In vitro embriyo üretiminde; mikromanipülasyon, östrus takibi, pedigri analizi, sperm morfolojisinin değerlendirilmesi, oosit ve blastosist kalitesinin değerlendirilmesi, fertilizasyonun değerlendirilmesi, hücre takibi, ploidi tahmini, başarılı gebelik oranı yüksek embriyo seçimi ve optimal protokollerin geliştirilmesi dâhil birçok alanda uygulanma potansiyeline sahip yapay zekâ, aynı zamanda in vivo embriyo üretiminde prosedürlerin hassasiyetinin artırılmasıyla, yardımcı üreme teknolojilerinin (ART) verim ve erişebilirliğini de artıracaktır. Bu derlemede teknolojinin gelişen topluma en güncel getirisi olan yapay zekânın, embriyo üretimi aşamalarında rol aldığı çalışmalar konu alınmıştır. Fare blastosistlerinin morfolojik olarak otomatik sınıflandırılması, grup içindeki bireysel insan spermatozoon hareketliliğinin eş zamanlı video üzerinden analiz edilmesi ve ineklerde boyun etiketi ile hareket analiziyle östrus takibi gibi birçok farklı türle yapılan çalışmalara değinilmiştir. Küresel olarak hem bugünün hem de yarının söz sahibi olan embriyo üretimi ve geleceğin mimarı yapay zekâyı birleştirerek yardımcı üreme teknolojilerine yeni bir bakış açısı kazandırmak ve sektörün yakın geleceğine göz atmak amaçlanmıştır.

Kaynakça

  • Abraham, F. (2017). An overview on functional causes of infertility in cows. JFIV Reprod Med Genet, 5(2), 203.
  • Abdullah, K. A. L., Atazhanova, T., Chavez-Badiola, A. & Shivhare, S. B. (2023). Automation in ART: paving the way for the future of infertility treatment. Reproductive Sciences, 30(4), 1006-1016.
  • Adaş, E. & Erbay, B. (2022). Yapay zekâ sosyolojisi üzerine bir değerlendirme. Gaziantep University Journal of Social Sciences, 21(1), 326-337.
  • Akar, D. (2024). Computer Vision Nedir? Nerelerde Kullanılır? Bilginç IT Academy. https://bilginc.com/tr/blog/computer-vision-nedir-nerelerde-kullanilir-3410/.
  • Bulletti, F. M., Berrettini, M., Sciorio, R. & Bulletti, C. (2023). Artificial intelligence algorithms for optimizing assisted reproductive technology programs: A systematic review. Glob Transl Med, 2, 0308.
  • Calderón, G., Carrillo, C., Nakano, M., Acevedo, J. & Hernández, J. (2020). Automatic Estrus Cycle Identification System on Female Dogs Based on Deep Learning in Pattern Recognition12th Mexican Conference, MCPR 2020, Morelia, Mexico, 2020, 261-268.
  • Cengiz, M. & Tohumcu, V. (2021). Sütçü ineklerde östrus siklusunun, foliküler gelişimin ve ovulasyonun hormonal kontrolü. Veteriner Farmakoloji ve Toksikoloji Derneği Bülteni, 12(3), 168-180.
  • Chafai, N., Hayah, I., Houaga, I. & Badaoui, B. (2023). A review of machine learning models applied to genomic prediction in animal breeding. Frontiers in Genetics, 14, 1150596, 1-18.
  • Chavez-Badiola, A., Flores-Saiffe-Farías, A., Mendizabal-Ruiz, G., Drakeley, A. J. & Cohen, J. (2020). Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reproductive Biomedicine Online, 41(4), 585-593.
  • Danardono, G. B., Handayani, N., Louis, C. M., Polim, A. A., Sirait, B., Periastiningrum, G. & Sini, I. (2023). Embryo ploidy status classification through computer-assisted morphology assessment. AJOG Global Reports, 3(3), 1-9.
  • Davis, T. C. & White, R. R. (2020). Breeding animals to feed people: The many roles of animal reproduction in ensuring global food security. Theriogenology, 150, 27-33.
  • Diakiw, S. M., Hall, J. M. M., VerMilyea, M. D., Amin, J., Aizpurua, J., Giardini, L. & Perugini, M. (2022). Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Human Reproduction, 37(8), 1746-1759.
  • Dimitriadis, I., Zaninovic, N., Badiola, A. C. & Bormann, C. L. (2022). Artificial intelligence in the embryology laboratory: a review. Reproductive Biomedicine Online, 44(3), 435- 448.
  • Fernandez, E. I., Ferreira, A. S., Cecílio, M. H. M., Chéles, D. S., de Souza, R. C. M., Nogueira, M. F. G. & Rocha, J. C. (2020). Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. Journal of Assisted Reproduction and Genetics, 37(10), 2359-2376.
  • Feyeux, M., Reignier, A., Mocaer, M., Lammers, J., Meistermann, D., Barrière, P. & Fréour, T. (2020). Development of automated annotation software for human embryo morphokinetics. Human Reproduction, 35(3), 557-564.
  • Gökalp, Ö. M. (2022). Makine öğrenmesi. Gazi Üniversitesi, Gazi Bilişim Enstitüsü, Adli Bilişim Bölümü (9 Aralık 2023): https://doi.org/10.13140/RG.2.2.28042.44480.
  • Hafez, Y. M. (2015). Assisted reproductive technologies in farm animals. 2nd International Conference on the Modern Approaches in Livestock's Production Systems Alexandria, Egypt, Ekim 2015, 91-118.
  • Hansen, P. J. (2014). Current and future assisted reproductive technologies for mammalian farm animals. Current and Future Reproductive Technologies and World Food Production, 1-22.
  • Hemalatha, R. J., SonaShree, S. P., Thamizhvani, T. R. & Vijayabaskar, V. (2021). Detection Of Estrus In Bovine Using Machine Learning. In: 2021 7th International conference on Bio Signals, Images, and Instrumentation (ICBSII), 1-5.
  • Jahnke, M. M., West, J. K. & Youngs, C. R. (2014). Evaluation of In Vivo‐Derived Bovine Embryos. Bovine Reproduction, 733-748.
  • Jiang, V. S. & Bormann, C. L. (2023). Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade. Fertility and Sterility, 120(1), 17–23.
  • Jiang, V. S., Kartik, D., Thirumalaraju, P., Kandula, H., Kanakasabapathy, M. K., Souter, I. & Shafiee, H. (2023). Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks. Journal of Assisted Reproduction and Genetics, 40(2), 251-257.
  • Kara, U. & Bekyürek, T. (2019). Sığır Embriyolarının Gelişim Evreleri ve Kalite Değerlendirilmesi. International Journal of Eastern Mediterranean Agricultural Research, 2(1), 113-129.
  • Karaküçük, Y., Eker, S. (2018). Oftalmolojide Yapay Zeka ve Derin Öğrenme Uygulamaları. In: Şahin A. R, Doğan K, Sivri S. Editors. Sağlık Bilimlerinde Yapay Zeka. Ankara, Türkiye: Akademisyen Yayıncılık; 2018. pp. 123-143.
  • Karaşahin, T. (2017). Türkiye İçin Sığırlarda Embriyo Transferi Gerekli mi? Journal of Advances in VetBio Science and Techniques, 2(2), 30-33.
  • Kaymaz, M. (2019). Yardımcı Üreme Teknikleri. In Kaymaz M, Fındık M, Rişvanlı A, Köker A. Editors. Çiftlik Hayvanlarında Doğum ve Jinekoloji. 3rd ed. Malatya, Türkiye: Medipres Yayıncılık; 2019. pp.539-618.
  • Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P. & Hajirasouliha, I. (2019). Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digital Medicine, 2(21), 1-9.
  • Layek, S. S., Patil, S. P., Gorani, S., Karuppanasamy, K., Kishore, G. & Gupta, R. O. (2022). Ovum Pick-Up and In Vitro Embryo Production in Bovine. In: Kumaresan A. & Srivastava A. K. Editors. Frontier Technologies in Bovine Reproduction. Singapur: Springer Nature Singapore: 2022. pp.211-232.
  • Letterie, G. & Mac Donald, A. (2020). Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertility and Sterility, 114(5), 1026-1031.
  • Louis, C. M., Erwin, A., Handayani, N., Polim, A. A., Boediono, A. & Sini, I. (2021). Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF. Journal of Assisted Reproduction and Genetics, 38(7), 1627-1639.
  • Luvoni, G. C., Chigioni, S. & Beccaglia, M. (2006). Embryo production in dogs: from in vitro fertilization to cloning. Reproduction in Domestic Animals, 41(4), 286-290.
  • Matos, F. D., Rocha, J. C. & Nogueira, M. F. G. (2014). A method using artificial neural networks to morphologically assess mouse blastocyst quality. Journal of Animal Science and Technology, 56, 1-10.
  • Medenica, S., Zivanovic, D., Batkoska, L., Marinelli, S., Basile, G., Perino, A. & Zaami, S. (2022). The future is coming: artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes—the value of regulatory frameworks. Diagnostics, 12(12), 2979.
  • Mendizabal-Ruiz, G., Chavez-Badiola, A., Figueroa, I. A., Nuño, V. M., Farias, A. F. S., Valencia-Murilloa, R. & Cohen, J. (2022). Computer software (SiD) assisted real- time single sperm selection associated with fertilization and blastocyst formation. Reproductive BioMedicine Online, 45(4), 703-711.
  • Mirsky, S. K., Barnea, I., Levi, M., Greenspan, H. & Shaked, N. T. (2017). Automated analysis of individual sperm cells using stain‐free interferometric phase microscopy and machine learning. Cytometry Part A, 91(9), 893-900.
  • Palermo, G., Joris, H., Devroey, P. & Van Steirteghem, A. C. (1992). Pregnancies after intracytoplasmic injection of single spermatozoon into an oocyte. The Lancet, 340(8810), 17-18.
  • Pannu, A. (2015). Artificial intelligence and its application in different areas. Artificial Intelligence, 4(10), 79-84.
  • Pirim, A. G. H. (2006). Yapay zekâ. Yaşar Üniversitesi E-Dergisi, 1(1), 81-93.
  • Raes, A., Azari-Dolatabad, N., Athanasiou, G., Sadeghi, H., Andueza, S. G., Arcos, J. L., ... & Van Soom, A. (2023). Measuring cumulus expansion of mammalian oocytes: comparing the reliability of methods and how artificial intelligence could automate the measurement. (7 Mart 2024): https://doi.org/10.21203/rs.3.rs-2572620/v1.
  • Rabel, R. C., Marchioretto, P. V., Bangert, E. A., Wilson, K., Milner, D. J. & Wheeler, M. B. (2023). Pre-Implantation Bovine Embryo Evaluation—From Optics to Omics and Beyond. Animals, 13(13), 1-36.
  • Raimundo, J. M. & Cabrita, P. (2021). Artificial intelligence at assisted reproductive technology. Procedia Computer Science, 181, 442-447.
  • Rajendran, S., Brendel, M., Barnes, J., Zhan, Q., Malmsten, J. E., Zisimopoulos, P. & Hajirasouliha, I. (2023). Automatic Ploidy Prediction and Quality Assessment of Human Blastocyst Using Time-Lapse Imaging. bioRxiv The Preprint Server for Biology (29 Eylül 2023): https://doi.org/10.1101/2023.08.31.555741. Russell, S. J. & Norvig, P. (2010). Artificial intelligence a modern approach. London.
  • Salih, M., Austin, C., Warty, R. R., Tiktin, C., Rolnik, D. L., Momeni, M. & Horta, F. (2023). Embryo selection through artificial intelligence versus embryologists: a systematic review. Human Reproduction Open, 2023(3), hoad031.
  • Saragusty, J., Ajmone-Marsan, P., Sampino, S. & Modlinski, J. A. (2020). Reproductive biotechnology and critically endangered species: Merging in vitro gametogenesis with inner cell mass transfer. Theriogenology, 155, 176–184.
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  • Wang, J., Zhang, Y., Wang, J., Zhao, K., Li, X. & Liu, B. (2022). Using machine-learning technique for estrus onset detection in dairy cows from acceleration and location data acquired by a neck-tag. Biosystems Engineering, 214, 193-206.
  • Zaninovic, N. & Rosenwaks, Z. (2020). Artificial intelligence in human in vitro fertilization and embryology. Fertility and Sterility, 114(5), 914-920.
  • Zhang, Z., Liu, J., Wang, X., Zhao, Q., Zhou, C., Tan, M. & Sun, Y. (2016). Robotic pick- and-place of multiple embryos for vitrification. IEEE Robotics and Automation Letters, 2(2), 570-576.
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THE USE OF ARTIFICIAL INTELLIGENCE AT EMBRYO PRODUCTION

Yıl 2024, Cilt: 15 Sayı: 3, 150 - 158, 31.12.2024
https://doi.org/10.38137/vftd.1522154

Öz

Artificial intelligence, which has been taking part in human and animal embryo production stages since the 2000s, is one of the most popular topics with promising studies in recent years. Artificial intelligence, which has the potential to be applied in many areas including micromanipulation, estrus monitoring, pedigree analysis, evaluation of sperm morphology, evaluation of oocyte and blastocyst quality, evaluation of fertilization, cell tracking, ploidy estimation, selection of embryos with high successful pregnancy rates and development of optimal protocols in in vitro embryo production, will also increase the efficiency and accessibility of assisted reproductive technologies (ART) by increasing the precision of procedures in in vivo embryo production. This review focuses on studies in which artificial intelligence, the most recent contribution of technology to the developing society, and its role in embryo production. In this review, we have touched upon studies with many different species, such as automatic morphological classification of mouse blastocysts, simultaneous video analysis of individual human spermatozoon motility within a group, and estrus tracking in cows with neck tag movement analysis. By combining embryo production, which has a global say both today and tomorrow, and artificial intelligence, the architect of the future, it is aimed to gain a new perspective on assisted reproductive technologies and to look at the near future of the sector.

Kaynakça

  • Abraham, F. (2017). An overview on functional causes of infertility in cows. JFIV Reprod Med Genet, 5(2), 203.
  • Abdullah, K. A. L., Atazhanova, T., Chavez-Badiola, A. & Shivhare, S. B. (2023). Automation in ART: paving the way for the future of infertility treatment. Reproductive Sciences, 30(4), 1006-1016.
  • Adaş, E. & Erbay, B. (2022). Yapay zekâ sosyolojisi üzerine bir değerlendirme. Gaziantep University Journal of Social Sciences, 21(1), 326-337.
  • Akar, D. (2024). Computer Vision Nedir? Nerelerde Kullanılır? Bilginç IT Academy. https://bilginc.com/tr/blog/computer-vision-nedir-nerelerde-kullanilir-3410/.
  • Bulletti, F. M., Berrettini, M., Sciorio, R. & Bulletti, C. (2023). Artificial intelligence algorithms for optimizing assisted reproductive technology programs: A systematic review. Glob Transl Med, 2, 0308.
  • Calderón, G., Carrillo, C., Nakano, M., Acevedo, J. & Hernández, J. (2020). Automatic Estrus Cycle Identification System on Female Dogs Based on Deep Learning in Pattern Recognition12th Mexican Conference, MCPR 2020, Morelia, Mexico, 2020, 261-268.
  • Cengiz, M. & Tohumcu, V. (2021). Sütçü ineklerde östrus siklusunun, foliküler gelişimin ve ovulasyonun hormonal kontrolü. Veteriner Farmakoloji ve Toksikoloji Derneği Bülteni, 12(3), 168-180.
  • Chafai, N., Hayah, I., Houaga, I. & Badaoui, B. (2023). A review of machine learning models applied to genomic prediction in animal breeding. Frontiers in Genetics, 14, 1150596, 1-18.
  • Chavez-Badiola, A., Flores-Saiffe-Farías, A., Mendizabal-Ruiz, G., Drakeley, A. J. & Cohen, J. (2020). Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reproductive Biomedicine Online, 41(4), 585-593.
  • Danardono, G. B., Handayani, N., Louis, C. M., Polim, A. A., Sirait, B., Periastiningrum, G. & Sini, I. (2023). Embryo ploidy status classification through computer-assisted morphology assessment. AJOG Global Reports, 3(3), 1-9.
  • Davis, T. C. & White, R. R. (2020). Breeding animals to feed people: The many roles of animal reproduction in ensuring global food security. Theriogenology, 150, 27-33.
  • Diakiw, S. M., Hall, J. M. M., VerMilyea, M. D., Amin, J., Aizpurua, J., Giardini, L. & Perugini, M. (2022). Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Human Reproduction, 37(8), 1746-1759.
  • Dimitriadis, I., Zaninovic, N., Badiola, A. C. & Bormann, C. L. (2022). Artificial intelligence in the embryology laboratory: a review. Reproductive Biomedicine Online, 44(3), 435- 448.
  • Fernandez, E. I., Ferreira, A. S., Cecílio, M. H. M., Chéles, D. S., de Souza, R. C. M., Nogueira, M. F. G. & Rocha, J. C. (2020). Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. Journal of Assisted Reproduction and Genetics, 37(10), 2359-2376.
  • Feyeux, M., Reignier, A., Mocaer, M., Lammers, J., Meistermann, D., Barrière, P. & Fréour, T. (2020). Development of automated annotation software for human embryo morphokinetics. Human Reproduction, 35(3), 557-564.
  • Gökalp, Ö. M. (2022). Makine öğrenmesi. Gazi Üniversitesi, Gazi Bilişim Enstitüsü, Adli Bilişim Bölümü (9 Aralık 2023): https://doi.org/10.13140/RG.2.2.28042.44480.
  • Hafez, Y. M. (2015). Assisted reproductive technologies in farm animals. 2nd International Conference on the Modern Approaches in Livestock's Production Systems Alexandria, Egypt, Ekim 2015, 91-118.
  • Hansen, P. J. (2014). Current and future assisted reproductive technologies for mammalian farm animals. Current and Future Reproductive Technologies and World Food Production, 1-22.
  • Hemalatha, R. J., SonaShree, S. P., Thamizhvani, T. R. & Vijayabaskar, V. (2021). Detection Of Estrus In Bovine Using Machine Learning. In: 2021 7th International conference on Bio Signals, Images, and Instrumentation (ICBSII), 1-5.
  • Jahnke, M. M., West, J. K. & Youngs, C. R. (2014). Evaluation of In Vivo‐Derived Bovine Embryos. Bovine Reproduction, 733-748.
  • Jiang, V. S. & Bormann, C. L. (2023). Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade. Fertility and Sterility, 120(1), 17–23.
  • Jiang, V. S., Kartik, D., Thirumalaraju, P., Kandula, H., Kanakasabapathy, M. K., Souter, I. & Shafiee, H. (2023). Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks. Journal of Assisted Reproduction and Genetics, 40(2), 251-257.
  • Kara, U. & Bekyürek, T. (2019). Sığır Embriyolarının Gelişim Evreleri ve Kalite Değerlendirilmesi. International Journal of Eastern Mediterranean Agricultural Research, 2(1), 113-129.
  • Karaküçük, Y., Eker, S. (2018). Oftalmolojide Yapay Zeka ve Derin Öğrenme Uygulamaları. In: Şahin A. R, Doğan K, Sivri S. Editors. Sağlık Bilimlerinde Yapay Zeka. Ankara, Türkiye: Akademisyen Yayıncılık; 2018. pp. 123-143.
  • Karaşahin, T. (2017). Türkiye İçin Sığırlarda Embriyo Transferi Gerekli mi? Journal of Advances in VetBio Science and Techniques, 2(2), 30-33.
  • Kaymaz, M. (2019). Yardımcı Üreme Teknikleri. In Kaymaz M, Fındık M, Rişvanlı A, Köker A. Editors. Çiftlik Hayvanlarında Doğum ve Jinekoloji. 3rd ed. Malatya, Türkiye: Medipres Yayıncılık; 2019. pp.539-618.
  • Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P. & Hajirasouliha, I. (2019). Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digital Medicine, 2(21), 1-9.
  • Layek, S. S., Patil, S. P., Gorani, S., Karuppanasamy, K., Kishore, G. & Gupta, R. O. (2022). Ovum Pick-Up and In Vitro Embryo Production in Bovine. In: Kumaresan A. & Srivastava A. K. Editors. Frontier Technologies in Bovine Reproduction. Singapur: Springer Nature Singapore: 2022. pp.211-232.
  • Letterie, G. & Mac Donald, A. (2020). Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertility and Sterility, 114(5), 1026-1031.
  • Louis, C. M., Erwin, A., Handayani, N., Polim, A. A., Boediono, A. & Sini, I. (2021). Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF. Journal of Assisted Reproduction and Genetics, 38(7), 1627-1639.
  • Luvoni, G. C., Chigioni, S. & Beccaglia, M. (2006). Embryo production in dogs: from in vitro fertilization to cloning. Reproduction in Domestic Animals, 41(4), 286-290.
  • Matos, F. D., Rocha, J. C. & Nogueira, M. F. G. (2014). A method using artificial neural networks to morphologically assess mouse blastocyst quality. Journal of Animal Science and Technology, 56, 1-10.
  • Medenica, S., Zivanovic, D., Batkoska, L., Marinelli, S., Basile, G., Perino, A. & Zaami, S. (2022). The future is coming: artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes—the value of regulatory frameworks. Diagnostics, 12(12), 2979.
  • Mendizabal-Ruiz, G., Chavez-Badiola, A., Figueroa, I. A., Nuño, V. M., Farias, A. F. S., Valencia-Murilloa, R. & Cohen, J. (2022). Computer software (SiD) assisted real- time single sperm selection associated with fertilization and blastocyst formation. Reproductive BioMedicine Online, 45(4), 703-711.
  • Mirsky, S. K., Barnea, I., Levi, M., Greenspan, H. & Shaked, N. T. (2017). Automated analysis of individual sperm cells using stain‐free interferometric phase microscopy and machine learning. Cytometry Part A, 91(9), 893-900.
  • Palermo, G., Joris, H., Devroey, P. & Van Steirteghem, A. C. (1992). Pregnancies after intracytoplasmic injection of single spermatozoon into an oocyte. The Lancet, 340(8810), 17-18.
  • Pannu, A. (2015). Artificial intelligence and its application in different areas. Artificial Intelligence, 4(10), 79-84.
  • Pirim, A. G. H. (2006). Yapay zekâ. Yaşar Üniversitesi E-Dergisi, 1(1), 81-93.
  • Raes, A., Azari-Dolatabad, N., Athanasiou, G., Sadeghi, H., Andueza, S. G., Arcos, J. L., ... & Van Soom, A. (2023). Measuring cumulus expansion of mammalian oocytes: comparing the reliability of methods and how artificial intelligence could automate the measurement. (7 Mart 2024): https://doi.org/10.21203/rs.3.rs-2572620/v1.
  • Rabel, R. C., Marchioretto, P. V., Bangert, E. A., Wilson, K., Milner, D. J. & Wheeler, M. B. (2023). Pre-Implantation Bovine Embryo Evaluation—From Optics to Omics and Beyond. Animals, 13(13), 1-36.
  • Raimundo, J. M. & Cabrita, P. (2021). Artificial intelligence at assisted reproductive technology. Procedia Computer Science, 181, 442-447.
  • Rajendran, S., Brendel, M., Barnes, J., Zhan, Q., Malmsten, J. E., Zisimopoulos, P. & Hajirasouliha, I. (2023). Automatic Ploidy Prediction and Quality Assessment of Human Blastocyst Using Time-Lapse Imaging. bioRxiv The Preprint Server for Biology (29 Eylül 2023): https://doi.org/10.1101/2023.08.31.555741. Russell, S. J. & Norvig, P. (2010). Artificial intelligence a modern approach. London.
  • Salih, M., Austin, C., Warty, R. R., Tiktin, C., Rolnik, D. L., Momeni, M. & Horta, F. (2023). Embryo selection through artificial intelligence versus embryologists: a systematic review. Human Reproduction Open, 2023(3), hoad031.
  • Saragusty, J., Ajmone-Marsan, P., Sampino, S. & Modlinski, J. A. (2020). Reproductive biotechnology and critically endangered species: Merging in vitro gametogenesis with inner cell mass transfer. Theriogenology, 155, 176–184.
  • Si, K., Huang, B. & Jin, L. (2023). Application of artificial intelligence in gametes and embryos selection. Human Fertility, 26(4), 757-777.
  • Targosz, A., Myszor, D. & Mrugacz, G. (2023). Human oocytes image classification method based on deep neural networks. BioMedical Engineering OnLine, 22(1), 1-16.
  • Targosz, A., Przystałka, P., Wiaderkiewicz, R. & Mrugacz, G. (2021). Semantic segmentation of human oocyte images using deep neural networks. BioMedical Engineering OnLine, 20(1), 1-26.
  • Tekin, K., Yurdakök Dikmen, B., Kanca, H. & Guatteo, R. (2021). Precision livestock farming technologies: Novel direction of information flow. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 68(2), 193-212.
  • Uzun, Y., Hatipoğlu, M., Bütüner, R. & Calp, M. H. (2021). Yapay zekâ insan zekâsını geçebilecek mi.Uluslararası Mühendislik, Doğa ve Sosyal Bilimler Sempozyumu ISENS-21 Ana Teması “Enerji ve Toplum”. Batman Üniversitesi.
  • Wang, J., Zhang, Y., Wang, J., Zhao, K., Li, X. & Liu, B. (2022). Using machine-learning technique for estrus onset detection in dairy cows from acceleration and location data acquired by a neck-tag. Biosystems Engineering, 214, 193-206.
  • Zaninovic, N. & Rosenwaks, Z. (2020). Artificial intelligence in human in vitro fertilization and embryology. Fertility and Sterility, 114(5), 914-920.
  • Zhang, Z., Liu, J., Wang, X., Zhao, Q., Zhou, C., Tan, M. & Sun, Y. (2016). Robotic pick- and-place of multiple embryos for vitrification. IEEE Robotics and Automation Letters, 2(2), 570-576.
  • Zhao, M., Xu, M., Li, H., Alqawasmeh, O., Chung, J. P. W., Li, T. C. & Chan, D. Y. L. (2021). Application of convolutional neural network on early human embryo segmentation during in vitro fertilization. Journal of Cellular and Molecular Medicine, 25(5), 2633-2644.
Toplam 53 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Veteriner Doğum ve Jinekoloji
Bölüm Derleme
Yazarlar

Pelin Kutlu 0009-0005-3458-0044

Mustafa Kaymaz 0000-0001-6981-0229

Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 25 Temmuz 2024
Kabul Tarihi 18 Kasım 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 15 Sayı: 3

Kaynak Göster

APA Kutlu, P., & Kaymaz, M. (2024). EMBRİYO ÜRETİMİNDE YAPAY ZEKÂ KULLANIMI. Veteriner Farmakoloji Ve Toksikoloji Derneği Bülteni, 15(3), 150-158. https://doi.org/10.38137/vftd.1522154