دوره 9، شماره 2 - ( 1403 )
دوره 9 شماره 2 صفحات 117-105 |
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Nateghi M R, Nikzad H, Hassani Bafrani M. The use of Machine Learning for Human Sperm Selection and Success Rate in IVF Methods. SJMR 2024; 9 (2) : 6
URL: http://saremjrm.com/article-1-336-fa.html
URL: http://saremjrm.com/article-1-336-fa.html
ناطقی محمدرضا، نیک زاد حسین، حسنی بافرانی مهدی. استفاده از یادگیری ماشینی برای انتخاب اسپرم انسان و میزان موفقیت در روش های IVF. مجله تحقيقات پزشكي صارم. 1403; 9 (2) :105-117
1- مرکز تحقیقات زنان زایمان و ناباروری صارم، بیمارستان فوق تخصصی صارم، دانشگاه علوم پزشکی ایران، تهران، ایران و مرکز تحقیقات سلولی-مولکولی و سلول‌های بنیادی صارم، بیمارستان فوق تخصصی صارم تهران، ایران
2- مرکز تحقیقات گامتوژنزیس دانشگاه علوم پزشکی کاشان، کاشان، ایران
3- کمیته تحقیقات دانشجویی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران
2- مرکز تحقیقات گامتوژنزیس دانشگاه علوم پزشکی کاشان، کاشان، ایران
3- کمیته تحقیقات دانشجویی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران
چکیده: (1172 مشاهده)
هدف: ناباروری در واقع یک نگرانی مهم بهداشت جهانی است. کیفیت گامت ها نقش اساسی در تعیین میزان موفقیت چرخه های فناوری کمک باروری (ART) دارد. در باروری و پزشکی باروری معاصر، استفاده از یادگیری ماشینی به عنوان یک ابزار قدرتمند برای پردازش مجموعه داده‌های بزرگ پدیدار شده است که پتانسیل بهبود شیوه‌های ART موجود را ارائه می‌دهد. هدف از این مطالعه مروری، ارزیابی و تعیین کمیت ویژگی‌های اسپرم در انسان با استفاده از تکنیک‌های یادگیری ماشینی بود. این رویکرد می‌تواند به ارزیابی دقیق‌تر کیفیت گامت کمک کند، که منجر به بهبود تصمیم‌گیری و نرخ موفقیت بالقوه بالاتر در روش‌های ART می‌شود. با استفاده از توانایی‌های یادگیری ماشینی، محققان می‌توانند بینش‌های ارزشمندی در مورد کیفیت گامت‌ها به دست آورند و در نتیجه درمان‌های باروری را برای افراد و زوج‌هایی که مشکلات ناباروری را تجربه می‌کنند، بهینه کنند.
مواد و روش ها: ما یک جستجوی جامع در PubMed، Google Scholar و Scopus با استفاده از کلمات کلیدی "Machine Learning AND Quantification AND IVF" انجام دادیم. مقالات واجد شرایط در ابتدا بر اساس عناوین آنها غربالگری می شدند. پس از غربالگری عنوان، غربالگری دوم بر اساس چکیده مقالات منتخب انجام شد. در نهایت، مقالات کامل مطالعات باقی‌مانده برای اطمینان از اینکه معیارهای ورود ما را برآورده می‌کنند، بررسی شدند. از هر مطالعه واجد شرایط، ما اطلاعات زیر را استخراج کردیم: نویسنده(های) مطالعه، سال انتشار، و روش به کار گرفته شده برای ارزیابی کیفیت تخمک انسانی.
نتیجه گیری: توسعه یک سیستم یادگیری ماشینی آموزش دیده به درستی نیازمند توجه دقیق به کیفیت داده ها، اندازه گیری، اندازه نمونه و توافقنامه مسائل اخلاقی است.
مواد و روش ها: ما یک جستجوی جامع در PubMed، Google Scholar و Scopus با استفاده از کلمات کلیدی "Machine Learning AND Quantification AND IVF" انجام دادیم. مقالات واجد شرایط در ابتدا بر اساس عناوین آنها غربالگری می شدند. پس از غربالگری عنوان، غربالگری دوم بر اساس چکیده مقالات منتخب انجام شد. در نهایت، مقالات کامل مطالعات باقی‌مانده برای اطمینان از اینکه معیارهای ورود ما را برآورده می‌کنند، بررسی شدند. از هر مطالعه واجد شرایط، ما اطلاعات زیر را استخراج کردیم: نویسنده(های) مطالعه، سال انتشار، و روش به کار گرفته شده برای ارزیابی کیفیت تخمک انسانی.
نتیجه گیری: توسعه یک سیستم یادگیری ماشینی آموزش دیده به درستی نیازمند توجه دقیق به کیفیت داده ها، اندازه گیری، اندازه نمونه و توافقنامه مسائل اخلاقی است.
شماره‌ی مقاله: 6
نوع مقاله: مروری سیستماتيک |
موضوع مقاله:
ناباروری
دریافت: 1403/5/11 | پذیرش: 1403/6/10 | انتشار: 1403/9/29
دریافت: 1403/5/11 | پذیرش: 1403/6/10 | انتشار: 1403/9/29
فهرست منابع
1. Inhorn, M.C. and P. Patrizio, Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Human reproduction update, 2015. 21(4): p. 411-426. [DOI:10.1093/humupd/dmv016] [PMID]
2. Khatun, A., M.S. Rahman, and M.-G. Pang, Clinical assessment of the male fertility. Obstetrics & gynecology science, 2018. 61(2): p. 179-191. [DOI:10.5468/ogs.2018.61.2.179] [PMID] []
3. Agarwal, A., et al., A unique view on male infertility around the globe. Reproductive biology and endocrinology, 2015. 13(1): p. 1-9. [DOI:10.1186/s12958-015-0032-1] [PMID] []
4. Fauser, B.C., Towards the global coverage of a unified registry of IVF outcomes. Reproductive biomedicine online, 2019. 38(2): p. 133-137. [DOI:10.1016/j.rbmo.2018.12.001] [PMID]
5. Wilkinson, J., et al., Reproductive medicine: still more ART than science? BJOG: An International Journal of Obstetrics and Gynaecology, 2018. 126(2): p. 138-141. [DOI:10.1111/1471-0528.15409] [PMID]
6. You, J.B., et al., Machine learning for sperm selection. Nature Reviews Urology, 2021. 18(7): p. 387-403. [DOI:10.1038/s41585-021-00465-1] [PMID]
7. Oseguera-López, I., et al., Novel techniques of sperm selection for improving IVF and ICSI outcomes. Frontiers in cell and developmental biology, 2019. 7: p. 298. [DOI:10.3389/fcell.2019.00298] [PMID] []
8. Swain, J.E. and T.B. Pool, ART failure: oocyte contributions to unsuccessful fertilization. Human reproduction update, 2008. 14(5): p. 431-446. [DOI:10.1093/humupd/dmn025] [PMID]
9. Bungum, M. and K. Oleszczuk, Sperm DNA and ART (IUI, IVF, ICSI) Pregnancy. A Clinician's Guide to Sperm DNA and Chromatin Damage, 2018: p. 393-410. [DOI:10.1007/978-3-319-71815-6_21]
10. Vaughan, D.A. and D. Sakkas, Sperm selection methods in the 21st century. Biology of reproduction, 2019. 101(6): p. 1076-1082. [DOI:10.1093/biolre/ioz032] [PMID]
11. Asali, A., et al., The possibility of integrating motile sperm organelle morphology examination (MSOME) with intracytoplasmic morphologically-selected sperm injection (IMSI) when treating couples with unexplained infertility. Plos one, 2020. 15(5): p. e0232156. [DOI:10.1371/journal.pone.0232156] [PMID] []
12. Anbari, F., et al., Microfluidic sperm selection yields higher sperm quality compared to conventional method in ICSI program: A pilot study. Systems Biology in Reproductive Medicine, 2021. 67(2): p. 137-143. [DOI:10.1080/19396368.2020.1837994] [PMID]
13. Bartoov, B., et al., Real‐time fine morphology of motile human sperm cells is associated with IVF‐ICSI outcome. Journal of andrology, 2002. 23(1): p. 1-8. [DOI:10.1002/j.1939-4640.2002.tb02595.x] [PMID]
14. Baldini, D., et al., Sperm Selection for ICSI: Do We Have a Winner? Cells, 2021. 10(12): p. 3566. [DOI:10.3390/cells10123566] [PMID] []
15. Sunkara, S.K., et al., Association between the number of eggs and live birth in IVF treatment: an analysis of 400 135 treatment cycles. Human reproduction, 2011. 26(7): p. 1768-1774. [DOI:10.1093/humrep/der106] [PMID]
16. Li, H.W.R., et al., Role of baseline antral follicle count and anti-Mullerian hormone in prediction of cumulative live birth in the first in vitro fertilisation cycle: a retrospective cohort analysis. PloS one, 2013. 8(4): p. e61095. [DOI:10.1371/journal.pone.0061095] [PMID] []
17. Fatemi, H.M., et al., High ovarian response does not jeopardize ongoing pregnancy rates and increases cumulative pregnancy rates in a GnRH-antagonist protocol. Human Reproduction, 2013. 28(2): p. 442-452. [DOI:10.1093/humrep/des389] [PMID]
18. Broekmans, F., et al., A systematic review of tests predicting ovarian reserve and IVF outcome. Human reproduction update, 2006. 12(6): p. 685-718. [DOI:10.1093/humupd/dml034] [PMID]
19. Revelli, A., et al., The ovarian sensitivity index (OSI) significantly correlates with ovarian reserve biomarkers, is more predictive of clinical pregnancy than the total number of oocytes, and is consistent in consecutive IVF cycles. Journal of clinical medicine, 2020. 9(6): p. 1914. [DOI:10.3390/jcm9061914] [PMID] []
20. Fauser, B., K. Diedrich, and P. Devroey, Predictors of ovarian response: progress towards individualized treatment in ovulation induction and ovarian stimulation. Human reproduction update, 2008. 14(1): p. 1-14.
https://doi.org/10.1093/humupd/8.1.1
https://doi.org/10.1093/humupd/dmh009
https://doi.org/10.1093/humupd/dml059
https://doi.org/10.1093/humupd/dmm034 [DOI:10.1093/humupd/7.1.1] [PMID]
21. Liu, L., et al., Machine learning-based modeling of ovarian response and the quantitative evaluation of comprehensive impact features. Diagnostics, 2022. 12(2): p. 492. [DOI:10.3390/diagnostics12020492] [PMID] []
22. Letterie, G.S. and A. MacDonald, A computerized decision-support system for day to day management of ovarian stimulation cycles during in vitro fertilization. Fertility and Sterility, 2019. 112(3): p. e28. [DOI:10.1016/j.fertnstert.2019.07.206]
23. Fanton, M., et al., An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation. Fertility and Sterility, 2022. 118(1): p. 101-108. [DOI:10.1016/j.fertnstert.2022.04.003] [PMID]
24. Letterie, G. and A. Mac Donald, 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, 2020. 114(5): p. 1026-1031. [DOI:10.1016/j.fertnstert.2020.06.006] [PMID]
25. Haines, N., et al., Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity. PLoS One, 2019. 14(2): p. e0211735. [DOI:10.1371/journal.pone.0211735] [PMID] []
26. Diehl, P.U. and M. Cook, Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Frontiers in computational neuroscience, 2015. 9: p. 99. [DOI:10.3389/fncom.2015.00099] [PMID] []
27. Beam, A.L. and I.S. Kohane, Big data and machine learning in health care. Jama, 2018. 319(13): p. 1317-1318. [DOI:10.1001/jama.2017.18391] [PMID]
28. Goldenberg, S.L., G. Nir, and S.E. Salcudean, A new era: artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology, 2019. 16(7): p. 391-403. [DOI:10.1038/s41585-019-0193-3] [PMID]
29. Im, H., et al., Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning. Nature biomedical engineering, 2018. 2(9): p. 666-674. [DOI:10.1038/s41551-018-0265-3] [PMID] []
30. Lo, Y.-C., et al., Machine learning in chemoinformatics and drug discovery. Drug discovery today, 2018. 23(8): p. 1538-1546. [DOI:10.1016/j.drudis.2018.05.010] [PMID] []
31. Vamathevan, J., et al., Applications of machine learning in drug discovery and development. Nature reviews Drug discovery, 2019. 18(6): p. 463-477. [DOI:10.1038/s41573-019-0024-5] [PMID] []
32. Liu, Y. and M. Zhang, Neural network methods for natural language processing. 2018, MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info ….
33. Wang, R., et al., Artificial intelligence in reproductive medicine. Reproduction (Cambridge, England), 2019. 158(4): p. R139. [DOI:10.1530/REP-18-0523] [PMID] []
34. Chu, K.Y., et al., Artificial intelligence in reproductive urology. Current urology reports, 2019. 20: p. 1-6. [DOI:10.1007/s11934-019-0914-4] [PMID]
35. Zegers-Hochschild, F., et al., The international committee for monitoring assisted reproductive technology (ICMART) and the world health organization (WHO) revised glossary on ART terminology, 2009. Human reproduction, 2009. 24(11): p. 2683-2687. [DOI:10.1093/humrep/dep343] [PMID]
36. Sengul, Y., A. Bener, and A. Uyar, Emerging technologies for improving embryo selection: a systematic review. Advanced Health Care Technologies, 2015: p. 55-64. [DOI:10.2147/AHCT.S71272]
37. Lafuente, R., et al., Outdoor air pollution and sperm quality. Fertility and sterility, 2016. 106(4): p. 880-896. [DOI:10.1016/j.fertnstert.2016.08.022] [PMID]
38. Jensen, T.K., et al., High dietary intake of saturated fat is associated with reduced semen quality among 701 young Danish men from the general population. The American journal of clinical nutrition, 2013. 97(2): p. 411-418. [DOI:10.3945/ajcn.112.042432] [PMID]
39. Afeiche, M., et al., Dairy food intake in relation to semen quality and reproductive hormone levels among physically active young men. Human reproduction, 2013. 28(8): p. 2265-2275. [DOI:10.1093/humrep/det133] [PMID] []
40. Du Plessis, S.S., et al., The effect of obesity on sperm disorders and male infertility. Nature Reviews Urology, 2010. 7(3): p. 153-161. [DOI:10.1038/nrurol.2010.6] [PMID]
41. Sear, R., et al., Understanding variation in human fertility: what can we learn from evolutionary demography? Philosophical Transactions of the Royal Society B: Biological Sciences, 2016. 371(1692): p. 20150144. [DOI:10.1098/rstb.2015.0144] [PMID] []
42. Ozturk, S., Selection of competent oocytes by morphological criteria for assisted reproductive technologies. Molecular Reproduction and Development, 2020. 87(10): p. 1021-1036. [DOI:10.1002/mrd.23420] [PMID]
43. Lemseffer, Y., et al., Methods for Assessing Oocyte Quality: A Review of Literature. Biomedicines, 2022. 10(9): p. 2184. [DOI:10.3390/biomedicines10092184] [PMID] []
44. Uyar, A., et al. ROC based evaluation and comparison of classifiers for IVF implantation prediction. in International Conference on Electronic Healthcare. 2009. Springer. [DOI:10.1007/978-3-642-11745-9_17]
45. Chen, C.-C., et al. Knowledge discovery on in vitro fertilization clinical data using particle swarm optimization. in 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering. 2009. IEEE. [DOI:10.1109/BIBE.2009.36]
46. Van Montfoort, A.P., et al., Reduced oxygen concentration during human IVF culture improves embryo utilization and cumulative pregnancy rates per cycle. Human reproduction open, 2020. 2020(1): p. hoz036. [DOI:10.1093/hropen/hoz036] [PMID] []
47. Lehner, A., et al., Embryo density may affect embryo quality during in vitro culture in a microwell group culture dish. Archives of Gynecology and Obstetrics, 2017. 296: p. 345-353. [DOI:10.1007/s00404-017-4403-z] [PMID]
48. Hook, K.A., et al., The social shape of sperm: using an integrative machine-learning approach to examine sperm ultrastructure and collective motility. Proceedings of the Royal Society B, 2021. 288(1959): p. 20211553. [DOI:10.1098/rspb.2021.1553] [PMID] []
49. Roldan, E.R., Sperm competition and the evolution of sperm form and function in mammals. Reproduction in Domestic Animals, 2019. 54: p. 14-21. [DOI:10.1111/rda.13552] [PMID]
50. Matter, F., A Clinician's Guide to Sperm DNA and Chromatin Damage. 2018, Springer International Publishing. p. 393-410.
51. Rappa, K.L., et al., Sperm processing for advanced reproductive technologies: Where are we today? Biotechnology advances, 2016. 34(5): p. 578-587. [DOI:10.1016/j.biotechadv.2016.01.007] [PMID]
52. Younglai, E., et al., Sperm swim-up techniques and DNA fragmentation. Human reproduction, 2001. 16(9): p. 1950-1953. [DOI:10.1093/humrep/16.9.1950] [PMID]
53. Yamanaka, M., et al., Combination of density gradient centrifugation and swim-up methods effectively decreases morphologically abnormal sperms. Journal of Reproduction and Development, 2016. 62(6): p. 599-606. [DOI:10.1262/jrd.2016-112] [PMID] []
54. Repping, S., et al., Use of the total motile sperm count to predict total fertilization failure in in vitro fertilization. Fertility and sterility, 2002. 78(1): p. 22-28. [DOI:10.1016/S0015-0282(02)03178-3] [PMID]
55. Ribeiro, S., et al., Inter‐and intra‐laboratory standardization of TUNEL assay for assessment of sperm DNA fragmentation. Andrology, 2017. 5(3): p. 477-485. [DOI:10.1111/andr.12334] [PMID]
56. Daloglu, M.U. and A. Ozcan, Computational imaging of sperm locomotion. Biology of reproduction, 2017. 97(2): p. 182-188. [DOI:10.1093/biolre/iox086] [PMID] []
57. Engel, K.M., et al., Automated semen analysis by SQA Vision® versus the manual approach-A prospective double‐blind study. Andrologia, 2019. 51(1): p. e13149. [DOI:10.1111/and.13149] [PMID]
58. Mendizabal-Ruiz, G., et al., Computer software (SiD) assisted real-time single sperm selection associated with fertilization and blastocyst formation. Reproductive BioMedicine Online, 2022. 45(4): p. 703-711. [DOI:10.1016/j.rbmo.2022.03.036] [PMID]
59. Ranjini, K., A. Suruliandi, and S. Raja, Machine learning techniques for assisted reproductive technology: A review. Journal of Circuits, Systems and Computers, 2020. 29(11): p. 2030010. [DOI:10.1142/S021812662030010X]
60. Hamamoto, R., et al., Epigenetics analysis and integrated analysis of multiomics data, including epigenetic data, using artificial intelligence in the era of precision medicine. Biomolecules, 2019. 10(1): p. 62. [DOI:10.3390/biom10010062] [PMID] []
61. Hamamoto, R., Application of artificial intelligence for medical research. 2021, MDPI. p. 90. [DOI:10.3390/biom11010090] [PMID] []
62. Merican, Z.Z., U.K. Yusof, and N.L. Abdullah, Review on Embryo Selection Based on Morphology Using Machine Learning Methods. International Journal of Advances in Soft Computing & Its Applications, 2021. 13(2): p. 44-59.
63. Sarker, I.H., Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2021. 2(3): p. 160. [DOI:10.1007/s42979-021-00592-x] [PMID] []
64. Salman, M., et al., Artificial intelligence in bio-medical domain. International Journal of Advanced Computer Science and Applications, 2017. 8(8): p. 319-327. [DOI:10.14569/IJACSA.2017.080842]
65. DeRoos, D., Hadoop for dummies. 2014: John Wiley & Sons.
66. Asada, K., et al., Uncovering prognosis-related genes and pathways by multi-omics analysis in lung cancer. Biomolecules, 2020. 10(4): p. 524. [DOI:10.3390/biom10040524] [PMID] []
67. Dozen, A., et al., Image segmentation of the ventricular septum in fetal cardiac ultrasound videos based on deep learning using time-series information. Biomolecules, 2020. 10(11): p. 1526. [DOI:10.3390/biom10111526] [PMID] []
68. Kobayashi, K., et al., Fully-connected neural networks with reduced parameterization for predicting histological types of lung cancer from somatic mutations. Biomolecules, 2020. 10(9): p. 1249. [DOI:10.3390/biom10091249] [PMID] []
69. Komatsu, M., et al., Detection of cardiac structural abnormalities in fetal ultrasound videos using deep learning. Applied Sciences, 2021. 11(1): p. 371. [DOI:10.3390/app11010371]
70. Jinnai, S., et al., The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules, 2020. 10(8): p. 1123. [DOI:10.3390/biom10081123] [PMID] []
71. Hamamoto, R., et al., Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine. Cancers, 2020. 12(12): p. 3532. [DOI:10.3390/cancers12123532] [PMID] []
72. Komatsu, M., et al., Towards clinical application of artificial intelligence in ultrasound imaging. Biomedicines, 2021. 9(7): p. 720. [DOI:10.3390/biomedicines9070720] [PMID] []
73. Yamada, M., et al., Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Scientific reports, 2019. 9(1): p. 14465. [DOI:10.1038/s41598-019-50567-5] [PMID] []
74. Asada, K., et al., Single-cell analysis using machine learning techniques and its application to medical research. Biomedicines, 2021. 9(11): p. 1513. [DOI:10.3390/biomedicines9111513] [PMID] []
75. Sahli, H., An introduction to machine learning. TORUS 1-toward an open resource using Services: Cloud computing for environmental data, 2020: p. 61-74. [DOI:10.1002/9781119720492.ch7]
76. Vieira, S., W.H.L. Pinaya, and A. Mechelli, Introduction to machine learning, in Machine learning. 2020, Elsevier. p. 1-20. [DOI:10.1016/B978-0-12-815739-8.00001-8]
77. Mathews, S.C., et al., Digital health: a path to validation. NPJ digital medicine, 2019. 2(1): p. 38. [DOI:10.1038/s41746-019-0111-3] [PMID] []
78. Wainberg, M., et al., Deep learning in biomedicine. Nature biotechnology, 2018. 36(9): p. 829-838. [DOI:10.1038/nbt.4233] [PMID]
79. Secinaro, S., et al., The role of artificial intelligence in healthcare: a structured literature review. BMC medical informatics and decision making, 2021. 21: p. 1-23. [DOI:10.1186/s12911-021-01488-9] [PMID] []
80. Riordon, J., et al., Deep learning with microfluidics for biotechnology. Trends in biotechnology, 2019. 37(3): p. 310-324. [DOI:10.1016/j.tibtech.2018.08.005] [PMID]
81. LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436-444. [DOI:10.1038/nature14539] [PMID]
82. Webb, S., Deep learning for biology. Nature, 2018. 554(7693): p. 555-557. [DOI:10.1038/d41586-018-02174-z]
83. Sato, T., et al., A new deep‐learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure. Reproductive Medicine and Biology, 2022. 21(1): p. e12454. [DOI:10.1002/rmb2.12454] [PMID] []
84. Riordon, J., C. McCallum, and D. Sinton, Deep learning for the classification of human sperm. Computers in biology and medicine, 2019. 111: p. 103342. [DOI:10.1016/j.compbiomed.2019.103342] [PMID]
85. Ilhan, H.O., G. Serbes, and N. Aydin, Automated sperm morphology analysis approach using a directional masking technique. Computers in Biology and Medicine, 2020. 122: p. 103845. [DOI:10.1016/j.compbiomed.2020.103845] [PMID]
86. Ilhan, H.O., et al., A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods. Medical & biological engineering & computing, 2020. 58: p. 1047-1068. [DOI:10.1007/s11517-019-02101-y] [PMID]
87. Iqbal, I., G. Mustafa, and J. Ma, Deep learning-based morphological classification of human sperm heads. Diagnostics, 2020. 10(5): p. 325. [DOI:10.3390/diagnostics10050325] [PMID] []
88. Javadi, S. and S.A. Mirroshandel, A novel deep learning method for automatic assessment of human sperm images. Computers in biology and medicine, 2019. 109: p. 182-194. [DOI:10.1016/j.compbiomed.2019.04.030] [PMID]
89. Abbasi, A., E. Miahi, and S.A. Mirroshandel, Effect of deep transfer and multi-task learning on sperm abnormality detection. Computers in Biology and Medicine, 2021. 128: p. 104121. [DOI:10.1016/j.compbiomed.2020.104121] [PMID]
90. Patel, D.P., K.X. Gross, and J.M. Hotaling, Can artificial intelligence drive optimal sperm selection for in vitro fertilization? Fertility and Sterility, 2021. 115(4): p. 883. [DOI:10.1016/j.fertnstert.2021.02.004] [PMID]
91. Kruger, T.F., et al., A new computerized method of reading sperm morphology (strict criteria) is as efficient as technician reading. Fertility and sterility, 1993. 59(1): p. 202-209. [DOI:10.1016/S0015-0282(16)55640-4] [PMID]
92. Kruger, T.F., et al., A prospective study on the predictive value of normal sperm morphology as evaluated by computer (IVOS). Fertility and Sterility, 1996. 66(2): p. 285-291. [DOI:10.1016/S0015-0282(16)58455-6] [PMID]
93. Coetzee, K., et al., Clinical value of using an automated sperm morphology analyzer (IVOS). Fertility and sterility, 1999. 71(2): p. 222-225. [DOI:10.1016/S0015-0282(98)00465-8] [PMID]
94. Coetzee, K., T. Kruger, and C. Lombard, Repeatability and variance analysis on multiple computer‐assisted (IVOS*) sperm morphology readings. Andrologia, 1999. 31(3): p. 163-168.
https://doi.org/10.1111/j.1439-0272.1999.tb01404.x [DOI:10.1046/j.1439-0272.1999.00257.x] [PMID]
95. Tseng, K.-K., et al., Computer-assisted system with multiple feature fused support vector machine for sperm morphology diagnosis. BioMed research international, 2013. 2013. [DOI:10.1155/2013/687607] [PMID] []
96. Kanakasabapathy, M.K., et al., An automated smartphone-based diagnostic assay for point-of-care semen analysis. Science translational medicine, 2017. 9(382): p. eaai7863. [DOI:10.1126/scitranslmed.aai7863] [PMID] []
97. Hicks, S.A., et al. Predicting Sperm Motility and Morphology Using Deep Learning and Handcrafted Features. in MediaEval. 2019.
98. Goodson, S.G., et al., CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns. Biology of reproduction, 2017. 97(5): p. 698-708. [DOI:10.1093/biolre/iox120] [PMID] []
99. Hicks, S.A., et al., Machine learning-based analysis of sperm videos and participant data for male fertility prediction. Scientific reports, 2019. 9(1): p. 16770. [DOI:10.1038/s41598-019-53217-y] [PMID] []
100. Somasundaram, D. and M. Nirmala, Faster region convolutional neural network and semen tracking algorithm for sperm analysis. Computer Methods and Programs in Biomedicine, 2021. 200: p. 105918. [DOI:10.1016/j.cmpb.2020.105918] [PMID]
101. Suarez, S.S. and A. Pacey, Sperm transport in the female reproductive tract. Human reproduction update, 2006. 12(1): p. 23-37. [DOI:10.1093/humupd/dmi047] [PMID]
102. Eisenbach, M. and L.C. Giojalas, Sperm guidance in mammals-an unpaved road to the egg. Nature reviews Molecular cell biology, 2006. 7(4): p. 276-285. [DOI:10.1038/nrm1893] [PMID]
103. Hidayatullah, P., et al., Bull sperm tracking and machine learning-based motility classification. IEEE Access, 2021. 9: p. 61159-61170. [DOI:10.1109/ACCESS.2021.3074127]
104. Valiuškaitė, V., et al., Deep learning based evaluation of spermatozoid motility for artificial insemination. Sensors, 2020. 21(1): p. 72. [DOI:10.3390/s21010072] [PMID] []
105. Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [DOI:10.1109/CVPR.2017.243] []
106. Deng, J., et al. Imagenet: A large-scale hierarchical image database. in 2009 IEEE conference on computer vision and pattern recognition. 2009. Ieee. [DOI:10.1109/CVPR.2009.5206848]
107. Jayaraman, V., et al., Sperm processing by swim-up and density gradient is effective in elimination of sperm with DNA damage. Journal of assisted reproduction and genetics, 2012. 29: p. 557-563. [DOI:10.1007/s10815-012-9742-x] [PMID] []
108. Wang, Y., et al., Prediction of DNA integrity from morphological parameters using a single‐sperm DNA fragmentation index assay. Advanced Science, 2019. 6(15): p. 1900712. [DOI:10.1002/advs.201900712] [PMID] []
109. Evenson, D.P., The Sperm Chromatin Structure Assay (SCSA®) and other sperm DNA fragmentation tests for evaluation of sperm nuclear DNA integrity as related to fertility. Animal reproduction science, 2016. 169: p. 56-75. [DOI:10.1016/j.anireprosci.2016.01.017] [PMID]
110. McCallum, C., et al., Deep learning-based selection of human sperm with high DNA integrity. Communications biology, 2019. 2(1): p. 250. [DOI:10.1038/s42003-019-0491-6] [PMID] []
111. Chakrabarti, S., et al., Data mining curriculum: A proposal (version 1.0). Intensive working group of ACM SIGKDD curriculum committee, 2006. 140: p. 1-10.
112. Uyar, A., et al. 3P: Personalized pregnancy prediction in IVF treatment process. in Electronic Healthcare: First International Conference, eHealth 2008, London, UK, September 8-9, 2008. Revised Selected Papers 1. 2009. Springer.
113. Wang, R., et al., Artificial intelligence in reproductive medicine. Reproduction, 2019. 158(4): p. R139-R154. [DOI:10.1530/REP-18-0523] [PMID] []
114. Khosravi, P., et al., Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ digital medicine, 2019. 2(1): p. 21. [DOI:10.1038/s41746-019-0096-y] [PMID] []
115. Wang, R., et al., Artificial intelligence in reproductive medicine. Reproduction, 2019. 158(4): p. 139-154. [DOI:10.1530/REP-18-0523] [PMID] []
116. Cavalera, F., et al., Chromatin organization and timing of polar body I extrusion identify developmentally competent mouse oocytes. International Journal of Developmental Biology, 2019. 63(3-4-5): p. 245-251. [DOI:10.1387/ijdb.180362sg] [PMID]
117. Fernandez, E.I., et al., 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, 2020. 37: p. 2359-2376. [DOI:10.1007/s10815-020-01881-9] [PMID] []
118. Zaninovic, N. and Z. Rosenwaks, Artificial intelligence in human in vitro fertilization and embryology. Fertility and Sterility, 2020. 114(5): p. 914-920. [DOI:10.1016/j.fertnstert.2020.09.157] [PMID]
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