Application of artificial intelligence in gametes and embryos selection

References

• Abbasi, A., Miahi, E., & Mirroshandel, S. A. (2021). Effect of deep transfer and multi-task learning on sperm abnormality detection. Computers in Biology and Medicine, 128, 104121. https://doi.org/10.1016/j.compbiomed.2020.104121
   PubMed Web of Science ®Google Scholar
• Abhari, S., & Kawwass, J. F. (2021). Pregnancy and neonatal outcomes after transfer of mosaic embryos: A review. Journal of Clinical Medicine, 10(7), 1369. https://doi.org/10.3390/jcm10071369
   PubMed Web of Science ®Google Scholar
• Agarwal, A., Baskaran, S., Parekh, N., Cho, C.-L., Henkel, R., Vij, S., Arafa, M., Panner Selvam, M. K., & Shah, R. (2021). Male infertility. Lancet, 397(10271), 319–333. https://doi.org/10.1016/S0140-6736(20)32667-2
   PubMed Web of Science ®Google Scholar
• Agarwal, A., Henkel, R., Huang, C.-C., & Lee, M.-S. (2019). Automation of human semen analysis using a novel artificial intelligence optical microscopic technology. Andrologia, 51(11), e13440. https://doi.org/10.1111/and.13440
   PubMed Web of Science ®Google Scholar
• Agarwal, A., Panner Selvam, M. K., Sharma, R., Master, K., Sharma, A., Gupta, S., & Henkel, R. (2018). Home sperm testing device versus laboratory sperm quality analyzer: Comparison of motile sperm concentration. Fertility and Sterility, 110(7), 1277–1284. https://doi.org/10.1016/j.fertnstert.2018.08.049
   PubMed Web of Science ®Google Scholar
• Alegre, L., Bori, L., Valera, M., de los, Á., Gouveia Nogueira, M. F., Ferreira, A. S., Rocha, J. C., & Meseguer, M. (2020). First application of artificial neuronal networks for human live birth prediction on geri time-lapse monitoring system blastocyst images. Fertility and Sterility, 114(3), e140. https://doi.org/10.1016/j.fertnstert.2020.08.414
   Web of Science ®Google Scholar
• Alegre, L., Del Gallego, R., Bori, L., Loewke, K., Maddah, M., Aparicio-Ruiz, B., Palma-Govea, A. P., Marcos, J., & Meseguer, M. (2021). Assessment of embryo implantation potential with a cloud-based automatic software. Reproductive Biomedicine Online, 42(1), 66–74. https://doi.org/10.1016/j.rbmo.2020.09.032
   PubMed Web of Science ®Google Scholar
• Alpha Scientists in Reproductive Medicine and ESHRE Special Interest Group of Embryology. (2011). The Istanbul consensus workshop on embryo assessment: Proceedings of an expert meeting. Human Reproduction, 26(6), 1270–1283. https://doi.org/10.1093/humrep/der037
   Google Scholar
• Bamford, T., Barrie, A., Montgomery, S., Dhillon-Smith, R., Campbell, A., Easter, C., & Coomarasamy, A. (2022). Morphological and morphokinetic associations with aneuploidy: A systematic review and meta-analysis. Human Reproduction Update, 28(5), 656–686. https://doi.org/10.1093/humupd/dmac022
   PubMed Web of Science ®Google Scholar
• Berntsen, J., Rimestad, J., Lassen, J. T., Tran, D., & Kragh, M. F. (2022). Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLOS One, 17(2), e0262661. https://doi.org/10.1371/journal.pone.0262661
   PubMed Web of Science ®Google Scholar
• Bori, L., Dominguez, F., Fernandez, E. I., Del Gallego, R., Alegre, L., Hickman, C., Quiñonero, A., Nogueira, M. F. G., Rocha, J. C., & Meseguer, M. (2021). An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: A preliminary study. Reproductive Biomedicine Online, 42(2), 340–350. https://doi.org/10.1016/j.rbmo.2020.09.031
   PubMed Web of Science ®Google Scholar
• Bori, L., Paya, E., Alegre, L., Viloria, T. A., Remohi, J. A., Naranjo, V., & Meseguer, M. (2020). Novel and conventional embryo parameters as input data for artificial neural networks: An artificial intelligence model applied for prediction of the implantation potential. Fertility and Sterility, 114(6), 1232–1241. https://doi.org/10.1016/j.fertnstert.2020.08.023
   PubMed Web of Science ®Google Scholar
• Bormann, C. L., Kanakasabapathy, M. K., Thirumalaraju, P., Gupta, R., Pooniwala, R., Kandula, H., Hariton, E., Souter, I., Dimitriadis, I., Ramirez, L. B., Curchoe, C. L., Swain, J., Boehnlein, L. M., & Shafiee, H. (2020). Performance of a deep learning based neural network in the selection of human blastocysts for implantation. eLife, 9, e55301. https://doi.org/10.7554/eLife.55301
   PubMed Web of Science ®Google Scholar
• Cavalera, F., Zanoni, M., Merico, V., Bui, T. T. H., Belli, M., Fassina, L., Garagna, S., & Zuccotti, M. (2018). A neural network-based identification of developmentally competent or incompetent mouse fully-grown oocytes. Journal of Visualized Experiments, (133), e56668: 1–5. https://doi.org/10.3791/56668
   PubMed Web of Science ®Google Scholar
• Chavez-Badiola, A., Flores-Saiffe Farias, A., Mendizabal-Ruiz, G., Garcia-Sanchez, R., Drakeley, A. J., & Garcia-Sandoval, J. P. (2020). Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning. Scientific Reports, 10(1), 4394. https://doi.org/10.1038/s41598-020-61357-9
   PubMed Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.rbmo.2020.07.003
   PubMed Web of Science ®Google Scholar
• Chen, L., Li, W., Liu, Y., Peng, Z., Cai, L., Zhang, N., Xu, J., Wang, L., Teng, X., Yao, Y., Zou, Y., Ma, M., Liu, J., Lu, S., Sun, H., & Yao, B. (2022). Non-invasive embryo selection strategy for clinical IVF to avoid wastage of potentially competent embryos. Reproductive Biomedicine Online, 45(1), 26–34. https://doi.org/10.1016/j.rbmo.2022.03.006
   PubMed Web of Science ®Google Scholar
• Chen, T., Cheng, Y., Wang, J., Yang, Z., Zheng, W., Chen, D. Z., & Wu, J. (2022). Automating blastocyst formation and quality prediction in time-lapse imaging with adaptive key frame selection. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical image computing and computer assisted intervention – MICCAI 2022 (Vol. 13434, pp. 445–455). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-16440-8_43
   Google Scholar
• Chen, T.-J., Zheng, W.-L., Liu, C.-H., Huang, I., Lai, H.-H., & Liu, M. (2019). Using deep learning with large dataset of microscope images to develop an automated embryo grading system. Fertility & Reproduction, 01(01), 51–56. https://doi.org/10.1142/S2661318219500051
   Google Scholar
• Coticchio, G., Fiorentino, G., Nicora, G., Sciajno, R., Cavalera, F., Bellazzi, R., Garagna, S., Borini, A., & Zuccotti, M. (2021). Cytoplasmic movements of the early human embryo: Imaging and artificial intelligence to predict blastocyst development. Reproductive Biomedicine Online, 42(3), 521–528. https://doi.org/10.1016/j.rbmo.2020.12.008
   PubMed Web of Science ®Google Scholar
• Diakiw, S. M., Hall, J. M. M., VerMilyea, M. D., Amin, J., Aizpurua, J., Giardini, L., Briones, Y. G., Lim, A. Y. X., Dakka, M. A., Nguyen, T. V., Perugini, D., & 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), deac131–1759. https://doi.org/10.1093/humrep/deac131
   Web of Science ®Google Scholar
• Dimitriadis, I., Bormann, C. L., Thirumalaraju, P., Kanakasabapathy, M., Gupta, R., Pooniwala, R., Souter, I., Hsu, J. Y., Rice, S. T., Bhowmick, P., & Shafiee, H. (2019). Artificial intelligence-enabled system for embryo classification and selection based on image analysis. Fertility and Sterility, 111(4), e21. https://doi.org/10.1016/j.fertnstert.2019.02.064
   PubMed Web of Science ®Google Scholar
• Dirvanauskas, D., Maskeliunas, R., Raudonis, V., & Damasevicius, R. (2019). Embryo development stage prediction algorithm for automated time lapse incubators. Computer Methods and Programs in Biomedicine, 177, 161–174. https://doi.org/10.1016/j.cmpb.2019.05.027
   PubMed Web of Science ®Google Scholar
• Enatsu, N., Miyatsuka, I., An, L. M., Inubushi, M., Enatsu, K., Otsuki, J., Iwasaki, T., Kokeguchi, S., & Shiotani, M. (2022). A novel system based on artificial intelligence for predicting blastocyst viability and visualizing the explanation. Reproductive Medicine and Biology, 21(1), e12443. https://doi.org/10.1002/rmb2.12443
   PubMed Web of Science ®Google Scholar
• Finelli, R., Leisegang, K., Tumallapalli, S., Henkel, R., & Agarwal, A. (2021). The validity and reliability of computer-aided semen analyzers in performing semen analysis: A systematic review. Translational Andrology and Urology, 10(7), 3069–3079. https://doi.org/10.21037/tau-21-276
   PubMed Web of Science ®Google Scholar
• Firuzinia, S., Afzali, S. M., Ghasemian, F., & Mirroshandel, S. A. (2021). A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images. Computer Methods and Programs in Biomedicine, 201, 105946. https://doi.org/10.1016/j.cmpb.2021.105946
   PubMed Web of Science ®Google Scholar
• Fitz, V. W., Kanakasabapathy, M. K., Thirumalaraju, P., Kandula, H., Ramirez, L. B., Boehnlein, L., Swain, J. E., Curchoe, C. L., James, K., Dimitriadis, I., Souter, I., Bormann, C. L., & Shafiee, H. (2021). Should there be an “AI” in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm. Journal of Assisted Reproduction and Genetics, 38(10), 2663–2670. https://doi.org/10.1007/s10815-021-02318-7
   PubMed Web of Science ®Google Scholar
• Giscard d‘Estaing, S., Labrune, E., Forcellini, M., Edel, C., Salle, B., Lornage, J., & Benchaib, M. (2021). A machine learning system with reinforcement capacity for predicting the fate of an ART embryo. Systems Biology in Reproductive Medicine, 67(1), 64–78. https://doi.org/10.1080/19396368.2020.1822953
   PubMed Web of Science ®Google Scholar
• Goodson, S. G., White, S., Stevans, A. M., Bhat, S., Kao, C.-Y., Jaworski, S., Marlowe, T. R., Kohlmeier, M., McMillan, L., Zeisel, S. H., & O'Brien, D. A. (2017). CASAnova: A multiclass support vector machine model for the classification of human sperm motility patterns. Biology of Reproduction, 97(5), 698–708. https://doi.org/10.1093/biolre/iox120
   PubMed Web of Science ®Google Scholar
• Hariharan, R., He, P., Meseguer, M., Toschi, M., Rocha, J. C., Zaninovic, N., Malmsten, J., Zhan, Q., & Hickman, C. (2019). Artificial intelligence assessment of time-lapse images can predict with 77% accuracy whether a human embryo capable of achieving a pregnancy will miscarry. Fertility and Sterility, 112(3), e38–e39. https://doi.org/10.1016/j.fertnstert.2019.07.230
   Web of Science ®Google Scholar
• He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0
   PubMed Web of Science ®Google Scholar
• Hernández-González, J., Inza, I., Crisol-Ortíz, L., Guembe, M. A., Iñarra, M. J., & Lozano, J. A. (2018). Fitting the data from embryo implantation prediction: Learning from label proportions. Statistical Methods in Medical Research, 27(4), 1056–1066. https://doi.org/10.1177/0962280216651098
   PubMed Web of Science ®Google Scholar
• Hicks, S. A., Andersen, J. M., Witczak, O., Thambawita, V., Halvorsen, P., Hammer, H. L., Haugen, T. B., & Riegler, M. A. (2019). Machine learning-based analysis of sperm videos and participant data for male fertility prediction. Scientific Reports, 9(1), 16770. https://doi.org/10.1038/s41598-019-53217-y
   PubMedGoogle Scholar
• Huang, B., Tan, W., Li, Z., & Jin, L. (2021). An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data. Reproductive Biology and Endocrinology, 19(1), 185. https://doi.org/10.1186/s12958-021-00864-4
   PubMed Web of Science ®Google Scholar
• Huang, B., Zheng, S., Ma, B., Yang, Y., Zhang, S., & Jin, L. (2022). Using deep learning to predict the outcome of live birth from more than 10,000 embryo data. BMC Pregnancy and Childbirth, 22(1), 36. https://doi.org/10.1186/s12884-021-04373-5
   PubMed Web of Science ®Google Scholar
• Ilhan, H. O., Sigirci, I. O., Serbes, G., & Aydin, N. (2020). 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, 58(5), 1047–1068. https://doi.org/10.1007/s11517-019-02101-y
   PubMed Web of Science ®Google Scholar
• Iqbal, I., Mustafa, G., & Ma, J. (2020). Deep learning-based morphological classification of human sperm heads. Diagnostics, 10(5), E325. https://doi.org/10.3390/diagnostics10050325
   Web of Science ®Google Scholar
• Javadi, S., & Mirroshandel, S. A. (2019). A novel deep learning method for automatic assessment of human sperm images. Computers in Biology and Medicine, 109, 182–194. https://doi.org/10.1016/j.compbiomed.2019.04.030
   PubMed Web of Science ®Google Scholar
• Kanakasabapathy, M., Dimitriadis, I., Thirumalaraju, P., Bormann, C. L., Souter, I., Hsu, J., Thatcher, M. L., Veiga, C., & Shafiee, H. (2019). An inexpensive, automated artificial intelligence (AI) system for human embryo morphology evaluation and transfer selection. Fertility and Sterility, 111(4), e11. https://doi.org/10.1016/j.fertnstert.2019.02.047
   Web of Science ®Google Scholar
• Kanakasabapathy, M. K., Thirumalaraju, P., Bormann, C. L., Kandula, H., Dimitriadis, I., Souter, I., Yogesh, V., Kota Sai Pavan, S., Yarravarapu, D., Gupta, R., Pooniwala, R., & Shafiee, H. (2019). Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology. Lab on a Chip, 19(24), 4139–4145. https://doi.org/10.1039/c9lc00721k
   PubMed Web of Science ®Google Scholar
• Khalife, D., Abu-Musa, A., Khalil, A., & Ghazeeri, G. (2021). Towards the selection of embryos with the greatest implantation potential. Journal of Obstetrics and Gynaecology, 41(7), 1010–1015. https://doi.org/10.1080/01443615.2020.1835842
   PubMed Web of Science ®Google Scholar
• Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., Sigaras, A., Lavery, S., Cooper, L. A. D., Hickman, C., Meseguer, M., Rosenwaks, Z., Elemento, O., Zaninovic, N., & Hajirasouliha, I. (2019). Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digital Medicine, 2(1), 21. https://doi.org/10.1038/s41746-019-0096-y
   PubMedGoogle Scholar
• Kobori, Y., Pfanner, P., Prins, G. S., & Niederberger, C. (2016). Novel device for male infertility screening with single-ball lens microscope and smartphone. Fertility and Sterility, 106(3), 574–578. https://doi.org/10.1016/j.fertnstert.2016.05.027
   PubMed Web of Science ®Google Scholar
• Kragh, M. F., Rimestad, J., Berntsen, J., & Karstoft, H. (2019). Automatic grading of human blastocysts from time-lapse imaging. Computers in Biology and Medicine, 115, 103494. https://doi.org/10.1016/j.compbiomed.2019.103494
   PubMed Web of Science ®Google Scholar
• Leahy, B. D., Jang, W.-D., Yang, H. Y., Struyven, R., Wei, D., Sun, Z., Lee, K. R., Royston, C., Cam, L., Kalma, Y., Azem, F., Ben-Yosef, D., Pfister, H., & Needleman, D. (2020). Automated measurements of key morphological features of human embryos for IVF. Medical Image Computing and Computer-Assisted Intervention, 12265, 25–35. https://doi.org/10.1007/978-3-030-59722-1_3
   PubMedGoogle Scholar
• Lesani, A., Kazemnejad, S., Moghimi Zand, M., Azadi, M., Jafari, H., Mofrad, M. R. K., & Nosrati, R. (2020). Quantification of human sperm concentration using machine learning-based spectrophotometry. Computers in Biology and Medicine, 127, 104061. https://doi.org/10.1016/j.compbiomed.2020.104061
   PubMed Web of Science ®Google Scholar
• Letort, G., Eichmuller, A., Da Silva, C., Nikalayevich, E., Crozet, F., Salle, J., Minc, N., Labrune, E., Wolf, J.-P., Terret, M.-E., & Verlhac, M.-H. (2022). An interpres and versatile machine learning approach for oocyte phenotyping. Journal of Cell Science, 135(13), jcs.260281. https://doi.org/10.1242/jcs.260281
   Web of Science ®Google Scholar
• Liang, B., Gao, Y., Xu, J., Song, Y., Xuan, L., Shi, T., Wang, N., Hou, Z., Zhao, Y.-L., Huang, W. E., & Chen, Z.-J. (2019). Raman profiling of embryo culture medium to identify aneuploid and euploid embryos. Fertility and Sterility, 111(4), 753–762.e1. https://doi.org/10.1016/j.fertnstert.2018.11.036
   PubMed Web of Science ®Google Scholar
• Liao, Q., Zhang, Q., Feng, X., Huang, H., Xu, H., Tian, B., Liu, J., Yu, Q., Guo, N., Liu, Q., Huang, B., Ma, D., Ai, J., Xu, S., & Li, K. (2021). Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring. Communications Biology, 4(1), 415. https://doi.org/10.1038/s42003-021-01937-1
   PubMed Web of Science ®Google Scholar
• Liu, G., Shi, H., Zhang, H., Zhou, Y., Sun, Y., Li, W., Huang, X., Jiang, Y., Fang, Y., & Yang, G. (2022). Fast noninvasive morphometric characterization of free human sperms using deep learning. Microscopy and Microanalysis, 7, 1–13. https://doi.org/10.1017/S1431927622012132
   Google Scholar
• Liu, Z., Huang, B., Cui, Y., Xu, Y., Zhang, B., Zhu, L., Wang, Y., Jin, L., & Wu, D. (2019). Multi-task deep learning with dynamic programming for embryo early development stage classification from time-lapse videos. IEEE Access. 7, 122153–122163. https://doi.org/10.1109/ACCESS.2019.2937765
   Web of Science ®Google Scholar
• Loewke, K., Cho, J. H., Brumar, C. D., Maeder-York, P., Barash, O., Malmsten, J. E., Zaninovic, N., Sakkas, D., Miller, K. A., Levy, M., & VerMilyea, M. D. (2022). Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos. Fertility and Sterility, 117(3), 528–535. https://doi.org/10.1016/j.fertnstert.2021.11.022
   PubMed Web of Science ®Google Scholar
• 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. https://doi.org/10.1007/s10815-021-02123-2
   PubMed Web of Science ®Google Scholar
• Lundin, K., & Park, H. (2020). Time-lapse technology for embryo culture and selection. Upsala Journal of Medical Sciences, 125(2), 77–84. https://doi.org/10.1080/03009734.2020.1728444
   PubMed Web of Science ®Google Scholar
• Malmsten, J., Zaninovic, N., Zhan, Q., Rosenwaks, Z., & Shan, J. (2021). Automated cell division classification in early mouse and human embryos using convolutional neural networks. Neural Computing and Applications, 33(7), 2217–2228. https://doi.org/10.1007/s00521-020-05127-8
   Web of Science ®Google Scholar
• Marín, R., & Chang, V. (2021). Impact of transfer learning for human sperm segmentation using deep learning. Computers in Biology and Medicine, 136, 104687. https://doi.org/10.1016/j.compbiomed.2021.104687
   PubMed Web of Science ®Google Scholar
• McCallum, C., Riordon, J., Wang, Y., Kong, T., You, J. B., Sanner, S., Lagunov, A., Hannam, T. G., Jarvi, K., & Sinton, D. (2019). Deep learning-based selection of human sperm with high DNA integrity. Communications Biology, 2(1), 250. https://doi.org/10.1038/s42003-019-0491-6
   PubMedGoogle Scholar
• Meseguer Escriva, M. M., Zaninovic, N., Marcelo, F. G. N., Oliana, O., Wilkinson, T., Benham-Whyte, L., Lavery, S., Hickman, C., & Rocha, J. C. (2018). Using artificial intelligence (AI) and time-lapse to improve human blastocyst morphology evaluation. Human Reproduction, 33(suppl_1), i125–i126 (Abstracts of the 34rd Annual Meeting of the European Society of Human Reproduction and Embryology). https://doi.org/10.1093/humrep/33.Supplement_1.1
   Google Scholar
• Milewski, R., Kuczyńska, A., Stankiewicz, B., & Kuczyński, W. (2017). How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis. Advances in Medical Sciences, 62(1), 202–206. https://doi.org/10.1016/j.advms.2017.02.001
   PubMed Web of Science ®Google Scholar
• Mirroshandel, S. A., Ghasemian, F., & Monji-Azad, S. (2016). Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment. Computer Methods and Programs in Biomedicine, 137, 215–229. https://doi.org/10.1016/j.cmpb.2016.09.013
   PubMed Web of Science ®Google Scholar
• Miyagi, Y., Habara, T., Hirata, R., & Hayashi, N. (2019a). Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age. Reproductive Medicine and Biology, 18(2), 190–203. https://doi.org/10.1002/rmb2.12266
   PubMed Web of Science ®Google Scholar
• Miyagi, Y., Habara, T., Hirata, R., & Hayashi, N. (2019b). Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age. Reproductive Medicine and Biology, 18(4), 344–356. https://doi.org/10.1002/rmb2.12284
   PubMed Web of Science ®Google Scholar
• Mortimer, D., Mortimer, S., & Horst, G. d (2015). The future of computer-aided sperm analysis. Asian Journal of Andrology, 17(4), 545. https://doi.org/10.4103/1008-682X.154312
   PubMed Web of Science ®Google Scholar
• Movahed, R. A., Mohammadi, E., & Orooji, M. (2019). Automatic segmentation of Sperm’s parts in microscopic images of human semen smears using concatenated learning approaches. Computers in Biology and Medicine, 109, 242–253. https://doi.org/10.1016/j.compbiomed.2019.04.032
   PubMed Web of Science ®Google Scholar
• Munné, S., Spinella, F., Grifo, J., Zhang, J., Beltran, M. P., Fragouli, E., & Fiorentino, F. (2020). Clinical outcomes after the transfer of blastocysts characterized as mosaic by high resolution next generation sequencing- further insights. European Journal of Medical Genetics, 63(2), 103741. https://doi.org/10.1016/j.ejmg.2019.103741
   PubMed Web of Science ®Google Scholar
• Payá, E., Bori, L., Colomer, A., Meseguer, M., & Naranjo, V. (2022). Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques. Computer Methods and Programs in Biomedicine, 221, 106895. https://doi.org/10.1016/j.cmpb.2022.106895
   PubMed Web of Science ®Google Scholar
• Pierré, W., Hervé, L., Paviolo, C., Mandula, O., Remondiere, V., Morales, S., Grudinin, S., Ray, P. F., Dhellemmes, M., Arnoult, C., & Allier, C. (2022). 3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework. Applied Optics, 61(12), 3337–3348. https://doi.org/10.1364/AO.453910
   PubMed Web of Science ®Google Scholar
• Raudonis, V., Paulauskaite-Taraseviciene, A., Sutiene, K., & Jonaitis, D. (2019). Towards the automation of early-stage human embryo development detection. BioMedical Engineering OnLine, 18(1), 120. https://doi.org/10.1186/s12938-019-0738-y
   PubMed Web of Science ®Google Scholar
• Riegler, M. A., Stensen, M. H., Witczak, O., Andersen, J. M., Hicks, S. A., Hammer, H. L., Delbarre, E., Halvorsen, P., Yazidi, A., Holst, N., & Haugen, T. B. (2021). Artificial intelligence in the fertility clinic: Status, pitfalls and possibilities. Human Reproduction, 36(9), 2429–2442. https://doi.org/10.1093/humrep/deab168
   PubMed Web of Science ®Google Scholar
• Riordon, J., McCallum, C., & Sinton, D. (2019). Deep learning for the classification of human sperm. Computers in Biology and Medicine, 111, 103342. https://doi.org/10.1016/j.compbiomed.2019.103342
   PubMed Web of Science ®Google Scholar
• Rosenwaks, Z. (2020). Artificial intelligence in reproductive medicine: A fleeting concept or the wave of the future? Fertility and Sterility, 114(5), 905–907. https://doi.org/10.1016/j.fertnstert.2020.10.002
   PubMed Web of Science ®Google Scholar
• Sato, T., Kishi, H., Murakata, S., Hayashi, Y., Hattori, T., Nakazawa, S., Mori, Y., Hidaka, M., Kasahara, Y., Kusuhara, A., Hosoya, K., Hayashi, H., & Okamoto, A. (2022). A new deep-learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure. Reproductive Medicine and Biology, 21(1), e12454. https://doi.org/10.1002/rmb2.12454
   PubMed Web of Science ®Google Scholar
• Sawada, Y., Sato, T., Nagaya, M., Saito, C., Yoshihara, H., Banno, C., Matsumoto, Y., Matsuda, Y., Yoshikai, K., Sawada, T., Ukita, N., & Sugiura-Ogasawara, M. (2021). Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth. Reproductive Biomedicine Online, 43(5), 843–852. https://doi.org/10.1016/j.rbmo.2021.05.002
   PubMed Web of Science ®Google Scholar
• Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., Smith, T. R., & Arnaout, O. (2018). An introduction and overview of machine learning in neurosurgical care. Acta Neurochirurgica, 160(1), 29–38. https://doi.org/10.1007/s00701-017-3385-8
   PubMed Web of Science ®Google Scholar
• Sfakianoudis, K., Maziotis, E., Grigoriadis, S., Pantou, A., Kokkini, G., Trypidi, A., Giannelou, P., Zikopoulos, A., Angeli, I., Vaxevanoglou, T., Pantos, K., & Simopoulou, M. (2022). Reporting on the value of artificial intelligence in predicting the optimal embryo for transfer: A systematic review including data synthesis. Biomedicines, 10(3), 697. https://doi.org/10.3390/biomedicines10030697
   PubMed Web of Science ®Google Scholar
• Spinella, F., Fiorentino, F., Biricik, A., Bono, S., Ruberti, A., Cotroneo, E., Baldi, M., Cursio, E., Minasi, M. G., & Greco, E. (2018). Extent of chromosomal mosaicism influences the clinical outcome of in vitro fertilization treatments. Fertility and Sterility, 109(1), 77–83. https://doi.org/10.1016/j.fertnstert.2017.09.025
   PubMed Web of Science ®Google Scholar
• Targosz, A., Przystałka, P., Wiaderkiewicz, R., & Mrugacz, G. (2021). Semantic segmentation of human oocyte images using deep neural networks. BioMedical Engineering OnLine, 20(1), 40. https://doi.org/10.1186/s12938-021-00864-w
   PubMed Web of Science ®Google Scholar
• Thirumalaraju, P., Hsu, J. Y., Bormann, C. L., Kanakasabapathy, M., Souter, I., Dimitriadis, I., Dickinson, K. A., Pooniwala, R., Gupta, R., Yogesh, V., & Shafiee, H. (2019). Deep learning-enabled blastocyst prediction system for cleavage stage embryo selection. Fertility and Sterility, 111(4), e29. https://doi.org/10.1016/j.fertnstert.2019.02.077
   Web of Science ®Google Scholar
• Tran, A., Cooke, S., Illingworth, P. J., & Gardner, D. K. (2018). Artificial intelligence as a novel approach for embryo selection. Fertility and Sterility, 110(4), e430. https://doi.org/10.1016/j.fertnstert.2018.08.034
   Web of Science ®Google Scholar
• Tran, D., Cooke, S., Illingworth, P. J., & Gardner, D. K. (2019). Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Human Reproduction (Oxford, England), 34(6), 1011–1018. https://doi.org/10.1093/humrep/dez064
   PubMed Web of Science ®Google Scholar
• Tsai, V. F., Zhuang, B., Pong, Y.-H., Hsieh, J.-T., & Chang, H.-C. (2020). Web- and artificial intelligence-based image recognition for sperm motility analysis: Verification study. JMIR Medical Informatics, 8(11), e20031. https://doi.org/10.2196/20031
   PubMed Web of Science ®Google Scholar
• Ueno, S., Berntsen, J., Ito, M., Uchiyama, K., Okimura, T., Yabuuchi, A., & Kato, K. (2021). Pregnancy prediction performance of an annotation-free embryo scoring system on the basis of deep learning after single vitrified-warmed blastocyst transfer: A single-center large cohort retrospective study. Fertility and Sterility, 116(4), 1172–1180. https://doi.org/10.1016/j.fertnstert.2021.06.001
   PubMed Web of Science ®Google Scholar
• Valiuškaitė, V., Raudonis, V., Maskeliūnas, R., Damaševičius, R., & Krilavičius, T. (2020). Deep learning based evaluation of spermatozoid motility for artificial insemination. Sensors, 21(1), 72. https://doi.org/10.3390/s21010072
   PubMed Web of Science ®Google Scholar
• VerMilyea, M., Hall, J. M. M., Diakiw, S. M., Johnston, A., Nguyen, T., Perugini, D., Miller, A., Picou, A., Murphy, A. P., & Perugini, M. (2020). Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Human Reproduction, 35(4), 770–784. https://doi.org/10.1093/humrep/deaa013
   PubMed Web of Science ®Google Scholar
• Wang, Y., Riordon, J., Kong, T., Xu, Y., Nguyen, B., Zhong, J., You, J. B., Lagunov, A., Hannam, T. G., Jarvi, K., & Sinton, D. (2019). Prediction of DNA integrity from morphological parameters using a single-sperm DNA fragmentation index assay. Advanced Science, 6(15), 1900712. https://doi.org/10.1002/advs.201900712
   Web of Science ®Google Scholar
• Wen, J.-Y., Liu, C.-F., Chung, M.-T., & Tsai, Y.-C. (2022). Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET). Taiwanese Journal of Obstetrics & Gynecology, 61(5), 837–846. https://doi.org/10.1016/j.tjog.2021.11.038
   PubMed Web of Science ®Google Scholar
• Wilkinson, J., Bhattacharya, S., Duffy, J., Kamath, M. S., Marjoribanks, J., Repping, S., Vail, A., van Wely, M., & Farquhar, C. M. (2019). Reproductive medicine: Still more ART than science? British Journal of Obstetrics and Gynaecology, 126(2), 138–141. https://doi.org/10.1111/1471-0528.15409
   Google Scholar
• World Health Organization & UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction. (2022). HRP annual report 2021. World Health Organization. https://apps.who.int/iris/handle/10665/358892
   Google Scholar
• Wu, G.-H., Smith-Geater, C., Galaz-Montoya, J. G., Gu, Y., Gupte, S. R., Aviner, R., Mitchell, P. G., Hsu, J., Miramontes, R., Wang, K. Q., Geller, N. R., Hou, C., Danita, C., Joubert, L.-M., Schmid, M. F., Yeung, S., Frydman, J., Mobley, W., Wu, C., Thompson, L. M., & Chiu, W. (2023). CryoET reveals organelle phenotypes in Huntington disease patient iPSC-derived and mouse primary neurons. Nature Communications, 14(1), 692. https://doi.org/10.1038/s41467-023-36096-w
   PubMed Web of Science ®Google Scholar
• Yang, L., Peavey, M., Kaskar, K., Chappell, N., Zhu, L., Devlin, D., Valdes, C., Schutt, A., Woodard, T., Zarutskie, P., Cochran, R., & Gibbons, W. E. (2022). Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics. F&S Reports, 3(2), 116–123. https://doi.org/10.1016/j.xfre.2022.04.004
   PubMedGoogle Scholar
• Yao, S., Mills, J. K., Abu Ajamieh, I., Li, H., & Zhang, X. (2020). Automatic three-dimensional imaging for blastomere identification in early-stage embryos based on brightfield microscopy. Optics and Lasers in Engineering, 130, 106093. https://doi.org/10.1016/j.optlaseng.2020.106093
   Web of Science ®Google Scholar
• You, J. B., McCallum, C., Wang, Y., Riordon, J., Nosrati, R., & Sinton, D. (2021). Machine learning for sperm selection. Nature Reviews. Urology, 18(7), 387–403. https://doi.org/10.1038/s41585-021-00465-1
   PubMed Web of Science ®Google Scholar
• Yüzkat, M., Ilhan, H. O., & Aydin, N. (2021). Multi-model CNN fusion for sperm morphology analysis. Computers in Biology and Medicine, 137, 104790. https://doi.org/10.1016/j.compbiomed.2021.104790
   PubMed Web of Science ®Google Scholar
• Zaninovic, N., Nohales, M., Zhan, Q., de Los Santos, Z. M. J., Sierra, J., Rosenwaks, Z., & Meseguer, M. (2019). A comparison of morphokinetic markers predicting blastocyst formation and implantation potential from two large clinical data sets. Journal of Assisted Reproduction and Genetics, 36(4), 637–646. https://doi.org/10.1007/s10815-018-1396-x
   PubMed Web of Science ®Google Scholar
• Zaninovic, N., & Rosenwaks, Z. (2020). Artificial intelligence in human in vitro fertilization and embryology. Fertility and Sterility, 114(5), 914–920. https://doi.org/10.1016/j.fertnstert.2020.09.157
   PubMed Web of Science ®Google Scholar
• Zhao, M., Xu, M., Li, H., Alqawasmeh, O., Chung, J. P. W., Li, T. C., Lee, T., Tang, P. M., & 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. https://doi.org/10.1111/jcmm.16288
   PubMed Web of Science ®Google Scholar
• Zheng, W., Zhang, S., Gu, Y., Gong, F., Kong, L., Lu, G., Lin, G., Liang, B., & Hu, L. (2021). Non-invasive metabolomic profiling of embryo culture medium using Raman spectroscopy with deep learning model predicts the blastocyst development potential of embryos. Frontiers in Physiology, 12, 777259. https://doi.org/10.3389/fphys.2021.777259
   PubMed Web of Science ®Google Scholar
• Zhou, X., Ma, L., Shang, Y., Xu, M., Fu, X., & Ding, H. (2016). Hybrid generative-discriminative learning for online tracking of sperm cell. Neurocomputing, 208, 218–224. https://doi.org/10.1016/j.neucom.2015.11.114
   Web of Science ®Google Scholar