Artificial intelligence in assisted reproductive techniques: comparative evaluation of deep learning architectures for bovine cumulus-oocyte complexes classification
Öz
Aim: Follicular quality is a key determinant of success in assisted reproductive technologies, directly affecting outcomes such as fertilization, embryo development, implantation, and live birth rates. However, conventional assessment of cumulus-oocyte complexes relies on subjective morphological evaluation, introducing variability and reducing consistency in clinical decision-making.
Materials and Methods: A comparative evaluation of various pre-trained deep learning architectures—including both convolutional neural networks and transformer-based models—was conducted for the automated morphological grading of bovine cumulus-oocyte complexes into four quality categories (Grade A–D). A dataset of 1,400 annotated images of cumulus-oocyte complexes, enhanced through data augmentation techniques to increase image diversity, was used for model training and validation.
Results: Among the tested architectures, Xception41 (a variant of convolutional neural networks) and Swin Transformer (a transformer-based model) achieved the highest performance, with test accuracies of 74.75% and 73.25%, and macro F1-scores of 0.75 and 0.74, respectively. While both models performed well in grading cumulus-oocyte complexes with distinct morphological features (Grades 3 and 4), classification accuracy decreased for the more subtle differences between Grades 1 and 2. Furthermore, most models exhibited signs of overfitting under the current training configuration.
Conclusion: This study demonstrates the potential of deep learning-based approaches to standardize and enhance the efficiency of cumulus-oocyte complexes evaluation in assisted reproductive technologies. Further optimization is needed to improve model generalization and to address challenges in grading morphologically similar follicular structures.
Anahtar Kelimeler
Artificial Intelligence Convolutional Neural Networks Xception41 Deep Learning Swin Transformer Cumulus-Oocyte Complexes Oocyte Classification Assisted Reproductive Technologies
Proje Numarası
BBAP.2022.006
Kaynakça
- Fainberg J, Kashanian JA. Recent advances in understanding and managing male infertility. F1000Res. 2019;8:670. doi:10.12688/f1000research.17076.1
- Crawford S, Fussman C, Bailey M, Bernson D, Jamieson DJ, Murray-Jordan M, et al. Estimates of lifetime infertility from three states: The Behavioral Risk Factor Surveillance System. J Womens Health (Larchmt). 2015;24(7):578-86. doi:10.1089/jwh.2014.5102
- Čegar B, Šipetić Grujičić S, Bjekić J, Vuksanović A, Bojanić N, Bartolović D, et al. Understanding the male perspective: evaluating quality of life and psychological distress in Serbian men undergoing infertility treatment. Life (Basel). 2023;13(9):1894. doi:10.3390/life13091894
- Uhde K, van Tol HTA, Stout TAE, Roelen BAJ. Metabolomic profiles of bovine cumulus cells and cumulus-oocyte-complex-conditioned medium during maturation in vitro. Sci Rep. 2018;8:9477. doi:10.1038/s41598-8-27829-9
- Richani D, Dunning KR, Thompson JG, Gilchrist RB. Metabolic co-dependence of the oocyte and cumulus cells: essential role in determining oocyte developmental competence. Hum Reprod Update. 2021;27(1):27-47. doi:10.1093/humupd/dmaa025
- Choi Y, Moon SH. Types and characteristics of stress coping in women undergoing infertility treatment in Korea. Int J Environ Res Public Health. 2023;20(3):2648. doi:10.3390/ijerph20032648
- Stojkovic M, Machado SA, Stojkovic P, Zakhartchenko V, Hutzler P, Gonçalves PB, Wolf E Mitochondrial distribution and adenosine triphosphate content of bovine oocytes before and after in vitro maturation: correlation with morphological criteria and developmental capacity after in vitro fertilization and culture. Biol Reprod. 2001;64(3):904-909
- Hartmann W, Pereira JFS. Biotechnics applied to bovine female. In: Bergstein-Galan TG, editor. Reproduction Biotechnology in Farm Animals. Brazil: AvidScience; 2018. p. 155-180
- Cavusoglu T, Gokhan A, Sirin C, Tomruk C, Kilic KD, Olmez E, et al. Classification of bovine cumulus-oocyte complexes with convolutional neural networks. Med Records. 2023;5(3):489-5. doi:10.37990/medr.1292782
- Chollet F. Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway (NJ): IEEE; 2017. p. 1800-7. doi:10.1109/CVPR.2017.195
- Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation [Internet]. arXiv; 2018 Feb 7 [cited 2025 Aug 10]. Available from: http://arxiv.org/abs/1802.02611.
- Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S. A ConvNet for the 2020s [Internet]. arXiv; 2022 Jan 10 [cited 2025 Aug 10]. Available from: http://arxiv.org/abs/2201.03545.
- Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, editors. Computer Vision – ECCV 2018. Lecture Notes in Computer Science, vol. 11211. Cham: Springer; 2018. p. 833-51. doi:10.1007/978-3-030-01234-2_49
- Park W, Schwendicke F, Krois J, Huh JK, Lee JH. Identification of dental implant systems using a large-scale multicenter data set. J Dent Res. 2023;102(7):727-33. doi:10.1177/00220345231160750
- Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway (NJ): IEEE; 2017. p. 2261-9. doi:10.1109/CVPR.2017.243
- Liao Q, Zhang Q, Feng X, Huang H, Xu H, Tian B, et al. Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring. Commun Biol. 2021;4:415. doi:10.1038/s42003-021-01937-1
- Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16x16 words: transformers for image recognition at scale [Internet]. arXiv; 2020 Oct 23 [cited 2025 Aug 10]. Available from: http://arxiv.org/abs/2010.11929
- Savaş S. Application of deep ensemble learning for palm disease detection in smart agriculture. Heliyon. 2024;10(17):e37141. doi:10.1016/j.heliyon.2024.e37141
- Bao H, Dong L, Piao S, Wei F. BEiT: BERT pre-training of image transformers [Internet]. arXiv; 2021 Jun 15 [cited 2025 Aug 10]. Available from: http://arxiv.org/abs/2106.08254
- ElMoaqet H, Janini R, Ryalat M, Al-Refai G, Abdulbaki Alshirbaji T, Jalal NA, et al. Using masked image modelling transformer architecture for laparoscopic surgical tool classification and localization. Sensors (Basel). 2025;25(10):3017. doi:10.3390/s25103017
- Kong F, Shi Z, Cao H, Hao Y, Cao Q. Self-supervised U-transformer network with mask reconstruction for metal artifact reduction. Phys Med Biol. 2025;70(6):065009. doi:10.1088/1361-6560/adbaae
- Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway (NJ): IEEE; 2021. p. 9992-10002. doi:10.1109/ICCV48922.2021.00986
- Omer AAM. Image classification based on vision transformer. J Comput Commun. 2024;12(4):49-59. doi:10.4236/jcc.2024.124005
- Li Y, Riganello F, Yu J, Vatrano M, Shen M, Cheng L, et al. The autonomic response following taVNS predicts changes in level of consciousness in DoC patients. Sci Rep. 2025;15:7317. doi:10.1038/s41598-024-84029-4
- Ishaq M, Raza S, Rehar H, Abadeen SeZu, Hussain D, Naqvi RA, et al. Assisting the human embryo viability assessment by deep learning for in vitro fertilization. Mathematics. 2023;11(9):2023. doi:10.3390/math11092023
- Arsalan M, Haider A, Choi J, Park KR. Detecting blastocyst components by artificial intelligence for human embryological analysis to improve success rate of in vitro fertilization. J Pers Med. 2022;12(2):124. doi:10.3390/jpm12020124
- Mushtaq A, Mumtaz M, Raza A, Salem N, Yasir MN. Artificial intelligence-based detection of human embryo components for assisted reproduction by in vitro fertilization. Sensors (Basel). 2022;22(19):7418. doi:10.3390/s22197418
- Jayashree P, Mitra S. Facilitating a deep approach to learning: an innovative case assessment technique. J Manag Organ. 2012;18(4):555-572. doi:10.5172/jmo.2012.18.4.555
- Allahbadia GN. Embryo transfer is the last frontier for deep machine learning and artificial intelligence in medically assisted reproduction (MAR). J Reprod. 2023;2(1):28-38. doi:10.58779/issn.2954-467X.tjor2023.v2.n1.18.
- Athanasiou G, Cerquides J, Raes A, Azari-Dolatabad N, Angel-Velez D, Van Soom A, et al. Detecting the area of bovine cumulus oocyte complexes using deep learning and semantic segmentation. In: Frontiers in Artificial Intelligence and Applications. Vol. 356, Artificial Intelligence Research and Development. Amsterdam: IOS Press; 2022. p. 249-258. doi:10.3233/FAIA220346
Yapay zekâ destekli üreme tekniklerinde: sığır kumulus-oosit komplekslerinin sınıflandırılmasına yönelik derin öğrenme mimarilerinin karşılaştırmalı değerlendirmesi
Öz
Amaç: Folikül kalitesi, yardımcı üreme teknolojilerindeki başarıyı belirleyen temel bir faktördür ve döllenme, embriyo gelişimi, implantasyon ve canlı doğum oranları gibi önemli sonuçları doğrudan etkiler. Ancak, kumulus-oosit komplekslerinin geleneksel değerlendirmesi, öznel morfolojik gözlemlere dayandığı için klinik karar süreçlerinde değişkenliğe ve tutarsızlığa neden olmaktadır.
Gereç ve Yöntem: Sığır kumulus-oosit komplekslerinin otomatik olarak morfolojik kaliteye göre dört kategoriye (A–D) ayrılması amacıyla, önceden eğitilmiş çeşitli derin öğrenme mimarileri—evrişimsel sinir ağları ve dönüştürücü tabanlı modeller dahil—karşılaştırmalı olarak değerlendirilmiştir. Görsel çeşitliliği artırmak amacıyla veri artırma teknikleri uygulanarak oluşturulan 1.400 etiketlenmiş kumulus-oosit kompleksi görüntülerinden oluşan veri seti, modellerin eğitimi ve doğrulaması için kullanılmıştır.
Bulgular: Test edilen mimariler arasında Xception41 (evrişimsel sinir ağları varyantı) ve Swin Transformer (dönüştürücü tabanlı bir model), sırasıyla %74,75 ve %73,25 test doğrulukları ile 0,75 ve 0,74 makro F1 skorlarına ulaşarak en yüksek performansı göstermiştir. Bu modeller, belirgin morfolojik özelliklere sahip 3. ve 4. derece kumulus-oosit komplekslerinde yüksek sınıflandırma başarısı gösterirken, 1. ve 2. dereceler arasındaki daha ince farkların ayırt edilmesinde zorlanmıştır. Ayrıca, mevcut eğitim konfigürasyonu altında çoğu modelde aşırı öğrenme eğilimi gözlemlenmiştir.
Sonuç: Bu çalışma, derin öğrenme tabanlı yaklaşımların yardımcı üreme teknolojileri kapsamında kumulus-oosit kompleksi değerlendirmesini standartlaştırma ve değerlendirme süreçlerinin etkinliğini artırma potansiyelini ortaya koymaktadır. Bununla birlikte, morfolojik olarak benzer foliküler yapılar arasındaki sınıflandırma zorluklarının aşılması ve model genelleme yeteneğinin artırılması için ilave optimizasyon gereklidir.
Anahtar Kelimeler
Yapay Zekâ Evrişimli Sinir Ağları Xception41 Derin Öğrenme Swin Transformer Kumulus-Oosit Kompleksi Oosit Sınıflandırması Yardımcı Üreme Teknikleri
Etik Beyan
Mert Can TEKEL, Pınar Entegre Et ve Un Sanayi A.Ş.’de görev yapmaktadır. Söz konusu kurumun bu çalışma üzerinde herhangi bir bilimsel etkisi bulunmamaktadır. Çalışmada kullanılan inek overleri, atık materyal kapsamında Pınar Entegre Et ve Un Sanayi A.Ş.’den temin edilmiştir. Şirket tarafından çalışmaya yönelik herhangi bir finansal veya maddi destek sağlanmamıştır. Yazarlar, bu çalışma kapsamında herhangi bir çıkar çatışması bulunmadığını beyan etmektedir.
Destekleyen Kurum
Bu çalışma, İzmir Bakırçay Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından BBAP.2022.006 Proje Numarası ile desteklenmiştir.
Proje Numarası
BBAP.2022.006
Teşekkür
Bu çalışma, İzmir Bakırçay Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından BBAP.2022.006 proje numarası ile desteklenmiştir. Sağladıkları katkılar ve destek için kurum yetkililerine teşekkür ederiz.
Kaynakça
- Fainberg J, Kashanian JA. Recent advances in understanding and managing male infertility. F1000Res. 2019;8:670. doi:10.12688/f1000research.17076.1
- Crawford S, Fussman C, Bailey M, Bernson D, Jamieson DJ, Murray-Jordan M, et al. Estimates of lifetime infertility from three states: The Behavioral Risk Factor Surveillance System. J Womens Health (Larchmt). 2015;24(7):578-86. doi:10.1089/jwh.2014.5102
- Čegar B, Šipetić Grujičić S, Bjekić J, Vuksanović A, Bojanić N, Bartolović D, et al. Understanding the male perspective: evaluating quality of life and psychological distress in Serbian men undergoing infertility treatment. Life (Basel). 2023;13(9):1894. doi:10.3390/life13091894
- Uhde K, van Tol HTA, Stout TAE, Roelen BAJ. Metabolomic profiles of bovine cumulus cells and cumulus-oocyte-complex-conditioned medium during maturation in vitro. Sci Rep. 2018;8:9477. doi:10.1038/s41598-8-27829-9
- Richani D, Dunning KR, Thompson JG, Gilchrist RB. Metabolic co-dependence of the oocyte and cumulus cells: essential role in determining oocyte developmental competence. Hum Reprod Update. 2021;27(1):27-47. doi:10.1093/humupd/dmaa025
- Choi Y, Moon SH. Types and characteristics of stress coping in women undergoing infertility treatment in Korea. Int J Environ Res Public Health. 2023;20(3):2648. doi:10.3390/ijerph20032648
- Stojkovic M, Machado SA, Stojkovic P, Zakhartchenko V, Hutzler P, Gonçalves PB, Wolf E Mitochondrial distribution and adenosine triphosphate content of bovine oocytes before and after in vitro maturation: correlation with morphological criteria and developmental capacity after in vitro fertilization and culture. Biol Reprod. 2001;64(3):904-909
- Hartmann W, Pereira JFS. Biotechnics applied to bovine female. In: Bergstein-Galan TG, editor. Reproduction Biotechnology in Farm Animals. Brazil: AvidScience; 2018. p. 155-180
- Cavusoglu T, Gokhan A, Sirin C, Tomruk C, Kilic KD, Olmez E, et al. Classification of bovine cumulus-oocyte complexes with convolutional neural networks. Med Records. 2023;5(3):489-5. doi:10.37990/medr.1292782
- Chollet F. Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway (NJ): IEEE; 2017. p. 1800-7. doi:10.1109/CVPR.2017.195
- Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation [Internet]. arXiv; 2018 Feb 7 [cited 2025 Aug 10]. Available from: http://arxiv.org/abs/1802.02611.
- Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S. A ConvNet for the 2020s [Internet]. arXiv; 2022 Jan 10 [cited 2025 Aug 10]. Available from: http://arxiv.org/abs/2201.03545.
- Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, editors. Computer Vision – ECCV 2018. Lecture Notes in Computer Science, vol. 11211. Cham: Springer; 2018. p. 833-51. doi:10.1007/978-3-030-01234-2_49
- Park W, Schwendicke F, Krois J, Huh JK, Lee JH. Identification of dental implant systems using a large-scale multicenter data set. J Dent Res. 2023;102(7):727-33. doi:10.1177/00220345231160750
- Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway (NJ): IEEE; 2017. p. 2261-9. doi:10.1109/CVPR.2017.243
- Liao Q, Zhang Q, Feng X, Huang H, Xu H, Tian B, et al. Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring. Commun Biol. 2021;4:415. doi:10.1038/s42003-021-01937-1
- Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16x16 words: transformers for image recognition at scale [Internet]. arXiv; 2020 Oct 23 [cited 2025 Aug 10]. Available from: http://arxiv.org/abs/2010.11929
- Savaş S. Application of deep ensemble learning for palm disease detection in smart agriculture. Heliyon. 2024;10(17):e37141. doi:10.1016/j.heliyon.2024.e37141
- Bao H, Dong L, Piao S, Wei F. BEiT: BERT pre-training of image transformers [Internet]. arXiv; 2021 Jun 15 [cited 2025 Aug 10]. Available from: http://arxiv.org/abs/2106.08254
- ElMoaqet H, Janini R, Ryalat M, Al-Refai G, Abdulbaki Alshirbaji T, Jalal NA, et al. Using masked image modelling transformer architecture for laparoscopic surgical tool classification and localization. Sensors (Basel). 2025;25(10):3017. doi:10.3390/s25103017
- Kong F, Shi Z, Cao H, Hao Y, Cao Q. Self-supervised U-transformer network with mask reconstruction for metal artifact reduction. Phys Med Biol. 2025;70(6):065009. doi:10.1088/1361-6560/adbaae
- Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway (NJ): IEEE; 2021. p. 9992-10002. doi:10.1109/ICCV48922.2021.00986
- Omer AAM. Image classification based on vision transformer. J Comput Commun. 2024;12(4):49-59. doi:10.4236/jcc.2024.124005
- Li Y, Riganello F, Yu J, Vatrano M, Shen M, Cheng L, et al. The autonomic response following taVNS predicts changes in level of consciousness in DoC patients. Sci Rep. 2025;15:7317. doi:10.1038/s41598-024-84029-4
- Ishaq M, Raza S, Rehar H, Abadeen SeZu, Hussain D, Naqvi RA, et al. Assisting the human embryo viability assessment by deep learning for in vitro fertilization. Mathematics. 2023;11(9):2023. doi:10.3390/math11092023
- Arsalan M, Haider A, Choi J, Park KR. Detecting blastocyst components by artificial intelligence for human embryological analysis to improve success rate of in vitro fertilization. J Pers Med. 2022;12(2):124. doi:10.3390/jpm12020124
- Mushtaq A, Mumtaz M, Raza A, Salem N, Yasir MN. Artificial intelligence-based detection of human embryo components for assisted reproduction by in vitro fertilization. Sensors (Basel). 2022;22(19):7418. doi:10.3390/s22197418
- Jayashree P, Mitra S. Facilitating a deep approach to learning: an innovative case assessment technique. J Manag Organ. 2012;18(4):555-572. doi:10.5172/jmo.2012.18.4.555
- Allahbadia GN. Embryo transfer is the last frontier for deep machine learning and artificial intelligence in medically assisted reproduction (MAR). J Reprod. 2023;2(1):28-38. doi:10.58779/issn.2954-467X.tjor2023.v2.n1.18.
- Athanasiou G, Cerquides J, Raes A, Azari-Dolatabad N, Angel-Velez D, Van Soom A, et al. Detecting the area of bovine cumulus oocyte complexes using deep learning and semantic segmentation. In: Frontiers in Artificial Intelligence and Applications. Vol. 356, Artificial Intelligence Research and Development. Amsterdam: IOS Press; 2022. p. 249-258. doi:10.3233/FAIA220346
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Histoloji ve Embriyoloji |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Proje Numarası | BBAP.2022.006 |
| Yayımlanma Tarihi | 8 Aralık 2025 |
| Gönderilme Tarihi | 8 Temmuz 2025 |
| Kabul Tarihi | 11 Ağustos 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 64 Sayı: 4 |
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