Research Article
Year 2023,
Volume: 5 Issue: 3, 489 - 95, 18.09.2023
Abstract
Project Number
THD-2021-23077
References
- 1-Dourou P, Gourounti K, Lykeridou A, et al. Quality of life among couples with a fertility related diagnosis. Clin Pract. 2023;13:251–63.
- 2. Ozturk, S. Selection of competent oocytes by morphological criteria for assisted reproductive technologies. Mol Reprod Dev. 2020;87:1021–36.
- 3. Esteves SC, Roque M, Sunkara SK, et al. Oocyte quantity, as well as oocyte quality, plays a significant role for the cumulative live birth rate of a POSEIDON criteria patient. Hum Reprod. 2019;34:2555–7.
- 4. Turathum B, Gao EM, Chian RC. The function of cumulus cells in oocyte growth and maturation and in subsequent ovulation and fertilization. Cells. 2021;10:2292.
- 5. Lewis N, Hinrichs K, Leese HJ, et al. Energy metabolism of the equine cumulus oocyte complex during in vitro maturation. Sci Rep. 2020;10:3493.
- 6. von Mengden L, Klamt F, Smitz J. Redox biology of human cumulus cells: basic concepts, impact on oocyte quality, and potential clinical use. Antioxid Redox Signal. 2020;32:522-35.
- 7. Lu X, Liu Y, Xu J, et al. Mitochondrial dysfunction in cumulus cells is related to decreased reproductive capacity in advanced-age women. Fertil Steril. 2022;118:393-404.
- 8. Yang Y, Cheung HH, Zhang C, et al. Melatonin as potential targets for delaying ovarian aging. Curr Drug Targets. 2019;20:16-28.
- 9. Wood TC, Wildt DE. Effect of the quality of the cumulus-oocyte complex in the domestic cat on the ability of oocytes to mature, fertilize and develop into blastocysts in vitro. J Reprod Fertil. 1997;110:355-60.
- 10. Lemseffer Y, Terret ME, Campillo C, Labrune E. Methods for assessing oocyte quality: a review of literature. Biomedicines. 2022;10:2184.
- 11. Ölmez E, Akdoğan V, Korkmaz M, Er O. Automatic segmentation of meniscus in multispectral MRI using regions with convolutional neural network (R-CNN). Journal of Digital Imaging, 2020;33:916-29.
- 12. Özbay Karakuş M, Er O. A comparative study on prediction of survival event of heart failure patients using machine learning algorithms. Neural Comput & Applic. 2022;34:13895-908.
- 13. Arslan H, Er O. A comparative study on COVID-19 prediction using deep learning and machine learning algorithms: a case study on performance analysis. Sakarya Univ J Comput Inform Sci. 2022;5:71-83. 14. Olmez E, Orhan E, Hiziroglu A. Deep learning in biomedical applications: detection of lung disease with convolutional neural networks. In: Jabbar MA, Abraham A, Dogan O, eds, Deep Learning in Biomedical and Health Informatics. 1st edition. CRC Press. 2021;97-115.
- 15. Lasiene K, Vitkus A, Valanciūte A, Lasys V. Morphological criteria of oocyte quality. Medicina (Kaunas). 2009;45:509-15.
- 16. Hu J, Ma X, Bao JC, et al. Insulin–transferrin–selenium (ITS) improves maturation of porcine oocytes in vitro. Zygote. 2011;19:191-7.
- 17. Chauhan MS, Singla SK, Palta P, et al. In vitro maturation and fertilization, and subsequent development of buffalo (Bubalus bubalis) embryos: effects of oocyte quality and type of serum. Reprod Fertil Dev. 1998;10:173-7. 18. Ozturk A, Allahverdi N, Saday F. Application of artificial intelligence methods for bovine gender prediction. Turk J Eng. 2022;6:54-62.
- 19. Choudhary KK, Kavya KM, Jerome A, Sharma RK. Advances in reproductive biotechnologies. Vet World. 2016;9:388-95.
- 20. Firuzinia S, Afzali SM, Ghasemian F, Mirroshandel SA. A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images. Comput Methods Programs Biomed. 2021;201:105946.
- 21. Targosz A, Przystałka P, Wiaderkiewicz R, Mrugacz G. Semantic segmentation of human oocyte images using deep neural networks. Biomed Eng Online. 2021;20:40.
- 22. Athanasiou G, Cerquides J, Raes A, et al. Detecting the area of bovine cumulus oocyte complexes using deep learning and semantic segmentation. In: Cortés A, Grimaldo F, Flaminio T, eds, Artificial Intelligence Research and Development. 2022;356:249-58.
- 23. Raudonis V, Paulauskaite-Taraseviciene A, Sutiene K, et al. Towards the automation of early-stage human embryo development detection. Biomed Eng Online. 2019;18:120.
- 24. Kragh MF, Rimestad J, Berntsen J, Karstoft H. Automatic grading of human blastocysts from time-lapse imaging. Comput Biol Med. 2019;115:103494. 25. Monge DF, Beltran CA. Classification of Eimeria species from digital micrographies using CNNs. 10th International Conference on Pattern Recognition Systems (ICPRS-2019), Tours, France. 2019;88-91.
- 26. Wang R, Pan W, Jin L, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019;158:R139-54.
Year 2023,
Volume: 5 Issue: 3, 489 - 95, 18.09.2023
Abstract
Aim: Determining oocyte quality is crucial for successful fertilization and embryonic development, and there is a serious correlation between live birth rates and oocyte quality. Parameters such as the regular/irregular formation of the cumulus cell layer around the oocyte, the number of cumulus cell layers and the homogeneity of the appearance of the ooplasm are used to determine the quality of the oocytes to be used in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) methods.
Material and Methods: In this study, classification processes have been carried out using convolutional neural networks (CNN), a deep learning method, on the images of the cumulus-oocyte complex selected based on the theoretical knowledge and professional experience of embryologists. A convolutional neural network with a depth of 4 is used. In each depth level, one convolution, one ReLU and one max-pooling layer are included. The designed network architecture is trained using the Adam optimization algorithm. The cumulus-oocyte complexes (n=400) used in the study were obtained by using the oocyte aspiration method from the ovaries of the bovine slaughtered at the slaughterhouse.
Results: The CNN-based classification model developed in this study showed promising results in classifying three-class image data in terms of cumulus-oocyte complex classification. The classification model achieved high accuracy, precision, and sensitivity values on the test dataset.
Conclusion: Continuous research and optimization of the model can further improve its performance and benefit the field of cumulus-oocyte complexes classification and oocyte quality assessment.
Supporting Institution
Ege University Scientific Research Projects Coordination Unit
Project Number
THD-2021-23077
Thanks
This study is supported by the Ege University Scientific Research Projects Coordination Unit, Scientific Research Project ID: THD-2021-23077.
References
- 1-Dourou P, Gourounti K, Lykeridou A, et al. Quality of life among couples with a fertility related diagnosis. Clin Pract. 2023;13:251–63.
- 2. Ozturk, S. Selection of competent oocytes by morphological criteria for assisted reproductive technologies. Mol Reprod Dev. 2020;87:1021–36.
- 3. Esteves SC, Roque M, Sunkara SK, et al. Oocyte quantity, as well as oocyte quality, plays a significant role for the cumulative live birth rate of a POSEIDON criteria patient. Hum Reprod. 2019;34:2555–7.
- 4. Turathum B, Gao EM, Chian RC. The function of cumulus cells in oocyte growth and maturation and in subsequent ovulation and fertilization. Cells. 2021;10:2292.
- 5. Lewis N, Hinrichs K, Leese HJ, et al. Energy metabolism of the equine cumulus oocyte complex during in vitro maturation. Sci Rep. 2020;10:3493.
- 6. von Mengden L, Klamt F, Smitz J. Redox biology of human cumulus cells: basic concepts, impact on oocyte quality, and potential clinical use. Antioxid Redox Signal. 2020;32:522-35.
- 7. Lu X, Liu Y, Xu J, et al. Mitochondrial dysfunction in cumulus cells is related to decreased reproductive capacity in advanced-age women. Fertil Steril. 2022;118:393-404.
- 8. Yang Y, Cheung HH, Zhang C, et al. Melatonin as potential targets for delaying ovarian aging. Curr Drug Targets. 2019;20:16-28.
- 9. Wood TC, Wildt DE. Effect of the quality of the cumulus-oocyte complex in the domestic cat on the ability of oocytes to mature, fertilize and develop into blastocysts in vitro. J Reprod Fertil. 1997;110:355-60.
- 10. Lemseffer Y, Terret ME, Campillo C, Labrune E. Methods for assessing oocyte quality: a review of literature. Biomedicines. 2022;10:2184.
- 11. Ölmez E, Akdoğan V, Korkmaz M, Er O. Automatic segmentation of meniscus in multispectral MRI using regions with convolutional neural network (R-CNN). Journal of Digital Imaging, 2020;33:916-29.
- 12. Özbay Karakuş M, Er O. A comparative study on prediction of survival event of heart failure patients using machine learning algorithms. Neural Comput & Applic. 2022;34:13895-908.
- 13. Arslan H, Er O. A comparative study on COVID-19 prediction using deep learning and machine learning algorithms: a case study on performance analysis. Sakarya Univ J Comput Inform Sci. 2022;5:71-83. 14. Olmez E, Orhan E, Hiziroglu A. Deep learning in biomedical applications: detection of lung disease with convolutional neural networks. In: Jabbar MA, Abraham A, Dogan O, eds, Deep Learning in Biomedical and Health Informatics. 1st edition. CRC Press. 2021;97-115.
- 15. Lasiene K, Vitkus A, Valanciūte A, Lasys V. Morphological criteria of oocyte quality. Medicina (Kaunas). 2009;45:509-15.
- 16. Hu J, Ma X, Bao JC, et al. Insulin–transferrin–selenium (ITS) improves maturation of porcine oocytes in vitro. Zygote. 2011;19:191-7.
- 17. Chauhan MS, Singla SK, Palta P, et al. In vitro maturation and fertilization, and subsequent development of buffalo (Bubalus bubalis) embryos: effects of oocyte quality and type of serum. Reprod Fertil Dev. 1998;10:173-7. 18. Ozturk A, Allahverdi N, Saday F. Application of artificial intelligence methods for bovine gender prediction. Turk J Eng. 2022;6:54-62.
- 19. Choudhary KK, Kavya KM, Jerome A, Sharma RK. Advances in reproductive biotechnologies. Vet World. 2016;9:388-95.
- 20. Firuzinia S, Afzali SM, Ghasemian F, Mirroshandel SA. A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images. Comput Methods Programs Biomed. 2021;201:105946.
- 21. Targosz A, Przystałka P, Wiaderkiewicz R, Mrugacz G. Semantic segmentation of human oocyte images using deep neural networks. Biomed Eng Online. 2021;20:40.
- 22. Athanasiou G, Cerquides J, Raes A, et al. Detecting the area of bovine cumulus oocyte complexes using deep learning and semantic segmentation. In: Cortés A, Grimaldo F, Flaminio T, eds, Artificial Intelligence Research and Development. 2022;356:249-58.
- 23. Raudonis V, Paulauskaite-Taraseviciene A, Sutiene K, et al. Towards the automation of early-stage human embryo development detection. Biomed Eng Online. 2019;18:120.
- 24. Kragh MF, Rimestad J, Berntsen J, Karstoft H. Automatic grading of human blastocysts from time-lapse imaging. Comput Biol Med. 2019;115:103494. 25. Monge DF, Beltran CA. Classification of Eimeria species from digital micrographies using CNNs. 10th International Conference on Pattern Recognition Systems (ICPRS-2019), Tours, France. 2019;88-91.
- 26. Wang R, Pan W, Jin L, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019;158:R139-54.
There are 23 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Clinical Sciences |
| Journal Section | Original Articles |
| Authors | |
| Project Number | THD-2021-23077 |
| Early Pub Date | July 6, 2023 |
| Publication Date | September 18, 2023 |
| Acceptance Date | May 29, 2023 |
| Published in Issue | Year 2023 Volume: 5 Issue: 3 |
Chief Editors
Assoc. Prof. Zülal Öner
Address: İzmir Bakırçay University, Department of Anatomy, İzmir, Türkiye
Assoc. Prof. Deniz Şenol
Address: Düzce University, Department of Anatomy, Düzce, Türkiye
E-mail: medrecsjournal@gmail.com
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Medical Records Association (Tıbbi Kayıtlar Derneği)
Address: Düzce / Türkiye
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