Gadadhar S, Alvarez Viar G, Hansen JN, Gong A, Kostarev A, Ialy-Radio C, et al. Tubulin glycylation controls axonemal dynein activity, flagellar beat, and male fertility. Science. 2021;371(6525):eabd4914. https://www.science.org/doi/abs/10.1126/science.abd4914.

Li X, Li C, Rahaman MM, Sun H, Li X, Wu J, et al. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev. 2022;55(6):4809–78. https://link.springer.com/article/10.1007/s10462-021-10121-0.

Marino JL, Moore VM, Rumbold AR, Davies MJ. Fertility treatments and the young women who use them: an Australian cohort study. Hum Reprod. 2011;26(2):473–9. https://academic.oup.com/humrep/article-abstract/26/2/473/593755.

Stouffs K, Tournaye H, Van der Elst J, Liebaers I, Lissens W. Is there a role for the nuclear export factor 2 gene in male infertility? Fertil Steril. 2008;90(5):1787–91. https://www.sciencedirect.com/science/article/pii/S001502820703467X.

Ström P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney DM, et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 2020;21(2):222–32. https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(19)30738-7/fulltext?13570.

Kriegeskorte N, Golan T. Neural network models and deep learning. Curr Biol. 2019;29(7):R231–6. https://www.cell.com/current-biology/pdf/S0960-9822(19)30204-0.pdf.

Hariton E, Pavlovic Z, Fanton M, Jiang VS. Applications of Artificial Intelligence in Ovarian Stimulation: A Tool for Improving Efficiency and Outcomes. Fertil Steril. 2023. https://www.sciencedirect.com/science/article/abs/pii/S0015028223005198. Accessed 19 May 2024.

Fanton M, Nutting V, Solano F, Maeder-York P, Hariton E, Barash O, et al. An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation. Fertil Steril. 2022;118(1):101–8. https://www.sciencedirect.com/science/article/pii/S0015028222002448.

Chandra S, Gourisaria MK, Gm H, Konar D, Gao X, Wang T, et al. Prolificacy Assessment of Spermatozoan via state-of-the-art Deep Learning Frameworks. IEEE Access. 2022;10:13715–27. https://ieeexplore.ieee.org/abstract/document/9693937/.

Spencer L, Fernando J, Akbaridoust F, Ackermann K, Nosrati R. Ensembled Deep Learning for the Classification of Human Sperm Head Morphology. Adv Intell Syst. 2022;4(10):2200111. https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202200111.

Riordon J, McCallum C, Sinton D. Deep learning for the classification of human sperm. Adv Intell Syst. 2019;111:103342. https://www.sciencedirect.com/science/article/pii/S0010482519302112.

Dobrovolny M, Benes J, Langer J, Krejcar O, Selamat A. Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled Dataset. Genes. 2023;14(2):451. https://www.mdpi.com/2073-4425/14/2/451.

Zhu R, Cui Y, Huang J, Hou E, Zhao J, Zhou Z, et al. YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection. Diagnostics. 2023;13(6):1100. https://www.mdpi.com/2075-4418/13/6/1100.

Zhang Z, Qi B, Ou S, Shi C. Real-Time Sperm Detection Using Lightweight YOLOv5. In: Proceedings of the 2022 IEEE 8th International Conference on Computer and Communications (ICCC), Sichuan, China, December 9-12, 2022. IEEE; 2022. p. 1829–1834. https://ieeexplore.ieee.org/abstract/document/10065602/.

Kahveci B, Önen S, Akal F, Korkusuz P. Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning. J Assist Reprod Genet. 2023;40(5):1187–95. https://link.springer.com/article/10.1007/s10815-023-02784-1.

Lee R, Witherspoon L, Robinson M, Lee JH, Duffy SP, Flannigan R, et al. Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning. Fertil Steril. 2022;118(1):90–9. https://www.sciencedirect.com/science/article/pii/S0015028222001959.

Targosz A, Myszor D, Mrugacz G. Human oocytes image classification method based on deep neural networks. Biomed Eng OnLine. 2023;22(1):92. https://link.springer.com/article/10.1186/s12938-023-01153-4.

Wu C, Yan W, Li H, Li J, Wang H, Chang S, et al. A classification system of day 3 human embryos using deep learning. Biomed Signal Process Control. 2021;70:102943. https://www.sciencedirect.com/science/article/pii/S1746809421005401.

Amitai T, Kan-Tor Y, Or Y, Shoham Z, Shofaro Y, Richter D, et al. Embryo classification beyond pregnancy: Early prediction of first trimester miscarriage using machine learning. J Assist Reprod Genet. 2023;40(2):309–22. https://link.springer.com/article/10.1007/s10815-022-02619-5.

Bormann CL, Kanakasabapathy MK, Thirumalaraju P, Gupta R, Pooniwala R, Kandula H, et al. Performance of a deep learning based neural network in the selection of human blastocysts for implantation. Elife. 2020;9:e55301. https://elifesciences.org/articles/55301.

Wan S, Zhao X, Niu Z, Dong L, Wu Y, Gu S, et al. Influence of ambient air pollution on successful pregnancy with frozen embryo transfer: A machine learning prediction model. Ecotoxicol Environ Saf. 2022;236:113444. https://www.sciencedirect.com/science/article/pii/S0147651322002846.

Mehrjerd A, Rezaei H, Eslami S, Ratna MB, Khadem Ghaebi N. Internal validation and comparison of predictive models to determine success rate of infertility treatments: a retrospective study of 2485 cycles. Sci Rep. 2022;12(1):7216. https://www.nature.com/articles/s41598-022-10902-9.

Blank C, Wildeboer RR, DeCroo I, Tilleman K, Weyers B, De Sutter P, et al. Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective. Fertil Steril. 2019;111(2):318–26. https://www.sciencedirect.com/science/article/pii/S0015028218321563.

Rienzi L, Cimadomo D, Delgado A, Minasi MG, Fabozzi G, Del Gallego R, et al. Time of morulation and trophectoderm quality are predictors of a live birth after euploid blastocyst transfer: a multicenter study. Fertil Steril. 2019;112(6):1080–1093. e1. https://www.sciencedirect.com/science/article/pii/S0015028219319302.

Lee LH, Bradburn E, Craik R, Yaqub M, Norris SA, Ismail LC, et al. Machine learning for accurate estimation of fetal gestational age based on ultrasound images. NPJ Digit Med. 2023;6(1):36. https://www.nature.com/articles/s41746-023-00774-2.

Makarious MB, Leonard HL, Vitale D, Iwaki H, Sargent L, Dadu A, et al. Multi-modality machine learning predicting Parkinson’s disease. NPJ Park Dis. 2022;8(1):35. https://www.nature.com/articles/s41531-022-00288-w.

Kiani A, Uyumazturk B, Rajpurkar P, Wang A, Gao R, Jones E, et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit Med. 2020;3(1):23. https://www.nature.com/articles/s41746-020-0232-8.

Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1(1):6. https://www.nature.com/articles/s41746-017-0013-1.

Zhou J, Hu B, Feng W, Zhang Z, Fu X, Shao H, et al. An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT. NPJ Digit Med. 2023;6(1):119. https://www.nature.com/articles/s41746-023-00866-z.

Deng Y, Lu L, Aponte L, Angelidi AM, Novak V, Karniadakis GE, et al. Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients. NPJ Digit Med. 2021;4(1):109. https://www.nature.com/articles/s41746-021-00480-x.

Xu Q, Zhan X, Zhou Z, Li Y, Xie P, Zhang S, et al. AI-based analysis of CT images for rapid triage of COVID-19 patients. NPJ Digit Med. 2021;4(1):75. https://www.nature.com/articles/s41746-021-00446-z.

Madani A, Ong JR, Tibrewal A, Mofrad MR. Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ Digit Med. 2018;1(1):59. https://www.nature.com/articles/s41746-018-0065-x.

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 Prog Biomed. 2021;201:105946. https://www.sciencedirect.com/science/article/pii/S0169260721000201.

Jiang VS, Kartik D, Thirumalaraju P, Kandula H, Kanakasabapathy MK, Souter I, et al. Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks. J Assist Reprod Genet. 2023;40(2):251–7. https://link.springer.com/article/10.1007/s10815-022-02685-9.

Goss DM, Vasilescu SA, Vasilescu PA, Cooke S, Kim SH, Sacks GP, et al. AI facilitated sperm detection in azoospermic samples for use in ICSI. medRxiv. 2023. https://www.medrxiv.org/content/10.1101/2023.10.25.23297520v1. Accessed 19 May 2024.

Kosela M, Aszyk J, Jarek M, Klimek J, Prokop T. Tracking of Spermatozoa by YOLOv5 Detection and StrongSORT with OSNet Tracker. 2022. https://ceur-ws.org/Vol-3583/paper41.pdf. Accessed 19 May 2024.

Yuzkat M, Ilhan HO, Aydin N. Detection of sperm cells by single-stage and two-stage deep object detectors. Biomed Signal Process Control. 2023;83:104630. https://www.sciencedirect.com/science/article/pii/S1746809423000630.

Zou S, Li C, Sun H, Xu P, Zhang J, Ma P, et al. TOD-CNN: an effective convolutional neural network for tiny object detection in sperm videos. Comput Biol Med. 2022;146:105543. https://www.sciencedirect.com/science/article/pii/S0010482522003353.

Mashaal AA, Eldosoky MA, Mahdy LN, Kadry AE. Automatic healthy sperm head detection using deep learning. Int J Adv Comput Sci Appl. 2022;13(4). https://www.proquest.com/openview/33e6627686425a66077a4b3dd0291196/1?pq-origsite=gscholar&cbl=5444811. Accessed 19 May 2024.

Siddiqui M, Haugen TB, Riegler MA, Hammer HL. Detecting Human Embryo Cleavage Stages Using YOLO V5 Object Detection Algorithm. In: Nordic Artificial Intelligence Research and Development: 4th Symposium of the Norwegian AI Society, NAIS 2022, Oslo, Norway, May 31–June 1, 2022, Revised Selected Papers. Springer Nature; 2023. pp. 81. https://library.oapen.org/bitstream/handle/20.500.12657/61287/1/978-3-031-17030-0.pdf#page=89.

Patil SN, Wali U, Swamy M, Nagaraj S, Patil N. Deep learning techniques for automatic classification and analysis of human in vitro fertilized (IVF) embryos. J Emerg Technol Innov Res. 2018;5(4):100–6. https://www.researchgate.net/profile/Sujata-Patil-9/publication/334596788_Issue_2_JETIR_ISSN-2349-5162_JETIR1802014_Journal_of_Emerging_Technologies_and_Innovative_Research_JETIR_wwwjetir/links/5d341af1299bf1995b3cf1c0/Issue-2-JETIR-ISSN-2349-5162-JETIR1802014-Journal-of-Emerging-Technologies-and-Innovative-Research-JETIR-wwwjetir.pdf.

Raudonis V, Paulauskaite-Taraseviciene A, Sutiene K, Jonaitis D. Towards the automation of early-stage human embryo development detection. Biomed Eng Online. 2019;18(1):1–20. https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-019-0738-y.

Dobrovolny M, Benes J, Krejcar O, Selamat A. Sperm-cell Detection Using YOLOv5 Architecture. In: International Work-Conference on Bioinformatics and Biomedical Engineering. Springer; 2022. pp. 319–330.  https://link.springer.com/chapter/10.1007/978-3-031-07802-6_27. 

Aristoteles A, Syarif A, Sutyarso S, Lumbanraja F. Identification of human sperm based on morphology using the you only look once version 4 algorithm. Int J Adv Comput Sci Appl. 2022;13(7):424–31. http://repository.lppm.unila.ac.id/43738/.

Sato T, Kishi H, Murakata S, Hayashi Y, Hattori T, Nakazawa S, et al. A new deep-learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure. Reprod Med Biol. 2022;21(1):e12454. https://onlinelibrary.wiley.com/doi/abs/10.1002/rmb2.12454.

Liu G, Shi H, Zhang H, Zhou Y, Sun Y, Li W, et al. Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning. Microsc Microanal. 2022;28(5):1767–79. https://academic.oup.com/mam/article-abstract/28/5/1767/6995548.

Dai C, Zhang Z, Jahangiri S, Shan G, Moskovstev S, Librach C, et al. Automated motility and morphology measurement of live spermatozoa. Andrology. 2021;9(4):1205–13. https://onlinelibrary.wiley.com/doi/abs/10.1111/andr.13002.

Liu H, Zhang Z, Gu Y, Dai C, Shan G, Song H, et al. Development and evaluation of a live birth prediction model for evaluating human blastocysts from a retrospective study. Elife. 2023;12:e83662. https://elifesciences.org/articles/83662.

Menapace W, Lathuilière S, Ricci E. Learning to cluster under domain shift. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVIII 16. Springer; 2020. pp. 736–52. https://link.springer.com/chapter/10.1007/978-3-030-58604-1_44.

Vidit V, Engilberge M, Salzmann M. Clip the gap: A single domain generalization approach for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, June 18-22, 2023. IEEE; 2023. p. 3219–29. http://openaccess.thecvf.com/content/CVPR2023/html/Vidit_CLIP_the_Gap_A_Single_Domain_Generalization_Approach_for_Object_CVPR_2023_paper.html.

Sun Y, Chong N, Ochiai H. Feature distribution matching for federated domain generalization. In: Asian Conference on Machine Learning. PMLR; 2023. pp. 942–57. https://proceedings.mlr.press/v189/sun23a.html.

Elsahar H, Gallé M. To annotate or not? predicting performance drop under domain shift. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, November 3-7, 2019. Assoc Comput Linguist. 2019. p. 2163–73. https://aclanthology.org/D19-1222/.

Singhal P, Walambe R, Ramanna S, Kotecha K. Domain adaptation: challenges, methods, datasets, and applications. IEEE Access. 2023;11:6973–7020. https://ieeexplore.ieee.org/abstract/document/10017290/.

You JB, McCallum C, Wang Y, Riordon J, Nosrati R, Sinton D. Machine learning for sperm selection. Nat Rev Urol. 2021;18(7):387–403. https://www.nature.com/articles/s41585-021-00465-1.

Gibney E. Is AI fuelling a reproducibility crisis in science. Nature. 2022;608(7922):250–1. https://www.nature.com/articles/d41586-022-02035-w.

McDermott MB, Wang S, Marinsek N, Ranganath R, Foschini L, Ghassemi M. Reproducibility in machine learning for health research: Still a ways to go. Sci Transl Med. 2021;13(586):eabb1655. https://www.science.org/doi/abs/10.1126/scitranslmed.abb1655.

Yang J, Soltan AA, Clifton DA. Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening. NPJ Digit Med. 2022;5(1):69. https://www.nature.com/articles/s41746-022-00614-9.

Article  PubMed  PubMed Central  Google Scholar 

Björndahl L, Barratt CL, Mortimer D, Agarwal A, Aitken RJ, Alvarez JG, et al. Standards in semen examination: publishing reproducible and reliable data based on high-quality methodology. Hum Reprod. 2022;37(11):2497–502. https://academic.oup.com/humrep/article-abstract/37/11/2497/6702083.

Leushuis E, Van Der Steeg JW, Steures P, Repping S, Bossuyt PM, Blankenstein MA, et al. Reproducibility and reliability of repeated semen analyses in male partners of subfertile couples. Fertil Steril. 2010;94(7):2631–5. https://www.sciencedirect.com/science/article/pii/S0015028210004589.

Chen B, Li Z, Ma Y, Wang N, Bai G. CIraCLoss: Intra-class Distance Loss Makes CNN Robust. In: Proceedings of the 2021 10th International Conference on Computing and Pattern Recognition, Shanghai, China, October 15-17, 2021. Assoc Comput Machinery. 2021. p. 290–5.  https://dl.acm.org/doi/abs/10.1145/3497623.3497670.

Wang Z, Hu Y, Chia LT. Image-to-class distance metric learning for image classification. In: Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part I 11. Springer; 2010. pp. 706–19. https://link.springer.com/chapter/10.1007/978-3-642-15549-9_51.

Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):1–48. https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-019-0197-0.pdf.

Cui Y, Zhou F, Lin Y, Belongie S. Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, June 27-30, 2016. IEEE; 2016. p. 1153–1162. http://openaccess.thecvf.com/content_cvpr_2016/html/Cui_Fine-Grained_Categorization_and_CVPR_2016_paper.html.

Saini M, Susan S. Deep transfer with minority data augmentation for imbalanced breast cancer dataset. Appl Soft Comput. 2020;97:106759. https://www.sciencedirect.com/science/article/pii/S1568494620306979.

Moreno-Barea FJ, Jerez JM, Franco L. Improving classification accuracy using data augmentation on small data sets. Expert Syst Appl. 2020;161:113696. https://www.sciencedirect.com/science/article/pii/S0957417420305200.

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, October 22-29, 2017. IEEE; 2017. p. 618–626. http://openaccess.thecvf.com/content_iccv_2017/html/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.html.