{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:11:36Z","timestamp":1776442296731,"version":"3.51.2"},"reference-count":77,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MCIU\/AEI\/10.13039\/50110001103","award":["PID2021-123960OB-I00"],"award-info":[{"award-number":["PID2021-123960OB-I00"]}]},{"name":"MCIU\/AEI\/10.13039\/50110001103","award":["PID2023-150070NB-I00"],"award-info":[{"award-number":["PID2023-150070NB-I00"]}]},{"name":"MCIU\/AEI\/10.13039\/50110001103","award":["101121309"],"award-info":[{"award-number":["101121309"]}]},{"name":"ERDF\/EU (FederaMed project)","award":["PID2021-123960OB-I00"],"award-info":[{"award-number":["PID2021-123960OB-I00"]}]},{"name":"ERDF\/EU (FederaMed project)","award":["PID2023-150070NB-I00"],"award-info":[{"award-number":["PID2023-150070NB-I00"]}]},{"name":"ERDF\/EU (FederaMed project)","award":["101121309"],"award-info":[{"award-number":["101121309"]}]},{"name":"Ministry of Science, Innovation","award":["PID2021-123960OB-I00"],"award-info":[{"award-number":["PID2021-123960OB-I00"]}]},{"name":"Ministry of Science, Innovation","award":["PID2023-150070NB-I00"],"award-info":[{"award-number":["PID2023-150070NB-I00"]}]},{"name":"Ministry of Science, Innovation","award":["101121309"],"award-info":[{"award-number":["101121309"]}]},{"name":"European Union (BAG-INTEL) project","award":["PID2021-123960OB-I00"],"award-info":[{"award-number":["PID2021-123960OB-I00"]}]},{"name":"European Union (BAG-INTEL) project","award":["PID2023-150070NB-I00"],"award-info":[{"award-number":["PID2023-150070NB-I00"]}]},{"name":"European Union (BAG-INTEL) project","award":["101121309"],"award-info":[{"award-number":["101121309"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Advances in artificial intelligence (AI) are transforming assisted reproductive technologies by significantly enhancing fertility diagnostics. This review focuses on integrating AI with Computer-Aided Sperm Analysis (CASA) systems to improve assessments of sperm motility, morphology, and DNA integrity. By employing a spectrum of techniques, from classic machine learning (ML), often valued for its interpretability and efficiency with structured data, to deep learning (DL), which excels at extracting intricate features directly from image and video data, the field now achieves more accurate, automated, and high-throughput evaluations. These advanced systems offer significant advantages, including enhanced objectivity, improved consistency over manual methods, and the ability to detect subtle predictive patterns not discernible by human observation. The emergence of extensive open datasets and big data analytics has enabled the development of more robust models. However, limitations persist, such as the dependency on large, high-quality annotated datasets for training DL models, potential challenges in model generalizability across diverse clinical settings, and the \u201cblack-box\u201d nature of some complex algorithms, alongside crucial needs for rigorous clinical validation, data standardization, and ethical management of sensitive information. Despite promising progress, these challenges must be addressed. Overall, this review outlines current innovations and future research directions essential for advancing personalized, efficient, and accessible fertility care.<\/jats:p>","DOI":"10.3390\/computation13060132","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T11:40:32Z","timestamp":1748864432000},"page":"132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Revolutionizing Sperm Analysis with AI: A Review of Computer-Aided Sperm Analysis Systems"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3102-8367","authenticated-orcid":false,"given":"Francisco J.","family":"Bald\u00e1n","sequence":"first","affiliation":[{"name":"Department of Computer Science and Programming Languages, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of M\u00e1laga, 29071 M\u00e1laga, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1927-8673","authenticated-orcid":false,"given":"Diego","family":"Garc\u00eda-Gil","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8809-8676","authenticated-orcid":false,"given":"Carlos","family":"Fernandez-Basso","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Granada, Periodista Daniel Saucedo Aranda, 18014 Granada, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/COMST.2023.3256323","article-title":"Artificial intelligence of things for smarter healthcare: A survey of advancements, challenges, and opportunities","volume":"25","author":"Baker","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","unstructured":"Tal, R., Talarczyk-Desole, J., Bentov, Y., Lin, Y.J., Fujiwara, T., and Tiboni, G.M. (2017). Advances in In Vitro Fertilization, Scientific Research Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Panner Selvam, M.K., Moharana, A.K., Baskaran, S., Finelli, R., Hudnall, M.C., and Sikka, S.C. (2024). Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis. Medicina, 60.","DOI":"10.3390\/medicina60020279"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.fertnstert.2023.05.157","article-title":"Artificial intelligence for sperm selection\u2014A systematic review","volume":"120","author":"Cherouveim","year":"2023","journal-title":"Fertil. Steril."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2400141","DOI":"10.1002\/aisy.202400141","article-title":"Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning","volume":"6","author":"Shahali","year":"2024","journal-title":"Adv. Intell. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e281","DOI":"10.1016\/j.fertnstert.2019.07.830","article-title":"Investigation of deep learning based detection of sperm morphological defects","volume":"112","author":"Yamashita","year":"2019","journal-title":"Fertil. Steril."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0015-0282(16)55161-9","article-title":"Improved fertilization rate in an in vitro fertilization program by egg yolk-treated sperm","volume":"58","author":"Barak","year":"1992","journal-title":"Fertil. Steril."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"431","DOI":"10.5124\/jkma.2007.50.5.431","article-title":"In Vitro Fertilization Program","volume":"50","author":"Moon","year":"2007","journal-title":"J. Korean Med Assoc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.fertnstert.2020.09.157","article-title":"Artificial intelligence in human in vitro fertilization and embryology","volume":"114","author":"Zaninovic","year":"2020","journal-title":"Fertil. Steril."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vaillancourt, C., and Lafond, J. (2009). Fertilization in vitro. Human Embryogenesis: Methods and Protocols, Humana Press.","DOI":"10.1007\/978-1-60327-009-0"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Soriano-\u00dabeda, C., Garc\u00eda-V\u00e1zquez, F.A., Romero-Aguirregomezcorta, J., and Mat\u00e1s, C. (2017). Improving porcine in vitro fertilization output by simulating the oviductal environment. Sci. Rep., 7.","DOI":"10.1038\/srep43616"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1111\/j.1749-6632.1988.tb22297.x","article-title":"Use of Micromanipulation for Increasing the Efficiency of Mammalian Fertilization In Vitro","volume":"541","author":"Gordon","year":"1988","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1177\/102490790000700309","article-title":"Recent advances in clinical aspects of in vitro fertilisation","volume":"6","author":"Leung","year":"2000","journal-title":"Hong Kong Med J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1094\/CM-2008-0317-01-RV","article-title":"Enhanced Efficiency Fertilizers for Improved Nutrient Management: Potato (Solanum Tuberosum)","volume":"7","author":"Hopkins","year":"2008","journal-title":"Crop Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hawkesford, M.J. (2012). Improving Nutrient Use Efficiency in Crops. eLS, John Wiley & Sons, Ltd.","DOI":"10.1002\/9780470015902.a0023734"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1080\/01904168709363671","article-title":"Fertilizer use efficiency","volume":"10","author":"Alexander","year":"1987","journal-title":"J. Plant Nutr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1016\/j.bpobgyn.2012.08.017","article-title":"In vitro fertilisation treatment and factors affecting success","volume":"26","author":"Huang","year":"2012","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.addr.2009.04.019","article-title":"Drug delivery for in vitro fertilization: Rationale, current strategies and challenges","volume":"61","author":"Gupta","year":"2009","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"264","DOI":"10.2741\/e242","article-title":"The state of the art of in vitro fertilization","volume":"E3","author":"Fechner","year":"2011","journal-title":"Front. Biosci."},{"key":"ref_20","first-page":"872","article-title":"Techniques, interrogations and results of medically assisted procreation","volume":"48","author":"Olivennes","year":"1993","journal-title":"Pediatrie"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Medenica, S., Zivanovic, D., Batkoska, L., Marinelli, S., Basile, G., Perino, A., Cucinella, G., Gullo, G., and Zaami, S. (2022). The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes\u2014The Value of Regulatory Frameworks. Diagnostics, 12.","DOI":"10.3390\/diagnostics12122979"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1093\/humrep\/deaa013","article-title":"Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF","volume":"35","author":"VerMilyea","year":"2020","journal-title":"Hum. Reprod."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1007\/s10815-021-02254-6","article-title":"Embryo selection with artificial intelligence: How to evaluate and compare methods?","volume":"38","author":"Kragh","year":"2021","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, F., Chen, Y., and Mai, Q. (2022). Multi-Omics Analysis and Machine Learning Prediction Model for Pregnancy Outcomes After Intracytoplasmic Sperm Injection\u2013in vitro Fertilization. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.924539"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hahne, F., Huber, W., Gentleman, R., Falcon, S., Gentleman, R., and Carey, V. (2008). Unsupervised machine learning. Bioconductor Case Studies, Springer.","DOI":"10.1007\/978-0-387-77240-0"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7","DOI":"10.2478\/slgr-2014-0043","article-title":"The use of principal component analysis and logistic regression in prediction of infertility treatment outcome","volume":"39","author":"Milewska","year":"2014","journal-title":"Stud. Log. GRAMMAR Rhetor."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Srivastava, D., Gupta, S., Kudavelly, S., Suryanarayana K, V., and Ga, R. (2021, January 1\u20135). Unsupervised Deep Learning based Longitudinal Follicular Growth Tracking during IVF Cycle using 3D Transvaginal Ultrasound in Assisted Reproduction. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual Conference.","DOI":"10.1109\/EMBC46164.2021.9630495"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sharma, N., Chakrabarti, S., Barak, Y., and Ellenbogen, A. (2020). The Sperm: Parameters and Evaluation. Innovations in Assisted Reproduction Technology, IntechOpen. Chapter 1.","DOI":"10.5772\/intechopen.77538"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1080\/14647273.2023.2256980","article-title":"Application of artificial intelligence in gametes and embryos selection","volume":"26","author":"Si","year":"2023","journal-title":"Hum. Fertil."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1038\/s41585-021-00465-1","article-title":"Machine learning for sperm selection","volume":"18","author":"You","year":"2021","journal-title":"Nat. Rev. Urol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"R139","DOI":"10.1530\/REP-18-0523","article-title":"Artificial intelligence in reproductive medicine","volume":"158","author":"Wang","year":"2019","journal-title":"Reproduction"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.theriogenology.2013.09.004","article-title":"Computer-assisted sperm analysis (CASA): Capabilities and potential developments","volume":"81","author":"Amann","year":"2014","journal-title":"Theriogenology"},{"key":"ref_34","unstructured":"World Health Organization (2021). WHO Laboratory Manual for the Examination and Processing of Human Semen, World Health Organization."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1007\/s10815-020-01881-9","article-title":"Artificial intelligence in the IVF laboratory: Overview through the application of different types of algorithms for the classification of reproductive data","volume":"37","author":"Fernandez","year":"2020","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.fertnstert.2019.05.019","article-title":"Artificial intelligence: Its applications in reproductive medicine and the assisted reproductive technologies","volume":"112","author":"Zaninovic","year":"2019","journal-title":"Fertil. Steril."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2030010","DOI":"10.1142\/S021812662030010X","article-title":"Machine Learning Techniques for Assisted Reproductive Technology: A Review","volume":"29","author":"Ranjini","year":"2020","journal-title":"J. Circ. Syst. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"deae108.429","DOI":"10.1093\/humrep\/deae108.429","article-title":"P-052 Machine learning-based sperm motility grading model","volume":"39","author":"Zhao","year":"2024","journal-title":"Hum. Reprod."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"61159","DOI":"10.1109\/ACCESS.2021.3074127","article-title":"Bull Sperm Tracking and Machine Learning-Based Motility Classification","volume":"9","author":"Hidayatullah","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Revollo, N.V., Sarmiento, G.N.R., Delrieux, C., Herrera, M., and Gonz\u00e1lez-Jos\u00e9, R. (2021). Supervised machine learning classification of human sperm head based on morphological features. Trends and Advancements of Image Processing and Its Applications, Springer.","DOI":"10.1007\/978-3-030-75945-2_9"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.compbiomed.2017.03.004","article-title":"Gold-standard for computer-assisted morphological sperm analysis","volume":"83","author":"Chang","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Iqbal, I., Mustafa, G., and Ma, J. (2020). Deep Learning-Based Morphological Classification of Human Sperm Heads. Diagnostics, 10.","DOI":"10.3390\/diagnostics10050325"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Riordon, J., McCallum, C., and Sinton, D. (2019). Deep learning for the classification of human sperm. Comput. Biol. Med., 111.","DOI":"10.1016\/j.compbiomed.2019.103342"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13715","DOI":"10.1109\/ACCESS.2022.3146334","article-title":"Prolificacy Assessment of Spermatozoan via State-of-the-Art Deep Learning Frameworks","volume":"10","author":"Chandra","year":"2022","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.compbiomed.2019.04.030","article-title":"A novel deep learning method for automatic assessment of human sperm images","volume":"109","author":"Javadi","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"16360","DOI":"10.3934\/math.2023838","article-title":"A review of different deep learning techniques for sperm fertility prediction","volume":"8","author":"Suleman","year":"2023","journal-title":"AIMS Math."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"McCallum, C., Riordon, J., Wang, Y., Kong, T., You, J.B., Sanner, S., Lagunov, A., Hannam, T.G., Jarvi, K., and Sinton, D. (2019). Deep learning-based selection of human sperm with high DNA integrity. Commun. Biol., 2.","DOI":"10.1038\/s42003-019-0491-6"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"60","DOI":"10.20473\/iabj.v1i2.35","article-title":"Computer Assisted Sperm Analysis: A Review","volume":"1","author":"Agustinus","year":"2020","journal-title":"Indones. Androl. Biomed. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1038\/s41597-023-02173-4","article-title":"VISEM-Tracking, a human spermatozoa tracking dataset","volume":"10","author":"Thambawita","year":"2023","journal-title":"Sci. Data"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"0308","DOI":"10.36922\/gtm.0308","article-title":"Artificial intelligence algorithms for optimizing assisted reproductive technology programs: A systematic review","volume":"2","author":"Bulletti","year":"2023","journal-title":"Glob. Transl. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.procs.2021.01.189","article-title":"Artificial intelligence at assisted reproductive technology","volume":"181","author":"Raimundo","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_52","first-page":"507","article-title":"Artificial Intelligence in Assisted Reproductive Technology Review","volume":"25","author":"Naser","year":"2021","journal-title":"Int. J. Prog. Sci. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1097\/GCO.0000000000000951","article-title":"Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient\u2019s journey","volume":"36","author":"Pavlovic","year":"2024","journal-title":"Curr. Opin. Obstet. Gynecol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1071\/RD17520","article-title":"CASA in the medical laboratory: CASA in diagnostic andrology and assisted conception","volume":"30","author":"Tomlinson","year":"2018","journal-title":"Reprod. Fertil. Dev."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1093\/biolre\/iox120","article-title":"CASAnova: A multiclass support vector machine model for the classification of human sperm motility patterns","volume":"97","author":"Goodson","year":"2017","journal-title":"Biol. Reprod."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1016\/j.fertnstert.2020.10.038","article-title":"Machine-learning algorithm incorporating capacitated sperm intracellular pH predicts conventional in vitro fertilization success in normospermic patients","volume":"115","author":"Gunderson","year":"2021","journal-title":"Fertil. Steril."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Bengio, S. (2004, January 4\u20138). Links between perceptrons, MLPs and SVMs. Proceedings of the 21st International Conference on Machine Learning, Banff, AB, Canada.","DOI":"10.1145\/1015330.1015415"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1095\/biolreprod.112.104653","article-title":"Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods","volume":"88","author":"Girela","year":"2013","journal-title":"Biol. Reprod."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"531","DOI":"10.3233\/THC-140816","article-title":"Seminal quality prediction using data mining methods","volume":"22","author":"Sahoo","year":"2014","journal-title":"Technol. Health Care"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"44","DOI":"10.18178\/ijmlc.2018.8.1.661","article-title":"Estimating the semen quality from life style using fuzzy radial basis functions","volume":"8","author":"Candemir","year":"2018","journal-title":"Int. J. Mach. Learn. Comput"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"27736","DOI":"10.1364\/OE.401925","article-title":"Learned SPARCOM: Unfolded deep super-resolution microscopy","volume":"28","author":"Eldar","year":"2020","journal-title":"Opt. Express"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1109\/TMI.2018.2840827","article-title":"Automated non-invasive measurement of single sperm\u2019s motility and morphology","volume":"37","author":"Dai","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Moretti, E., Signorini, C., Noto, D., Corsaro, R., and Collodel, G. (2022). The relevance of sperm morphology in male infertility. Front. Reprod. Health, 4.","DOI":"10.3389\/frph.2022.945351"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"deab130.010","DOI":"10.1093\/humrep\/deab130.010","article-title":"P\u2013011 Automated sperm morphology assessment using artificial intelligence technology","volume":"36","author":"Agarwal","year":"2021","journal-title":"Hum. Reprod."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"deae108.411","DOI":"10.1093\/humrep\/deae108.411","article-title":"P-034 Revolutionizing sperm morphology assessment: A novel artificial intelligence optic microscope (AIOM)-powered platform for consistent and reliable analysis in male infertility diagnosis","volume":"39","author":"Chang","year":"2024","journal-title":"Hum. Reprod."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1037\/h0031619","article-title":"Measuring nominal scale agreement among many raters","volume":"76","author":"Fleiss","year":"1971","journal-title":"Psychol. Bull."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Y\u00fczkat, M., Ilhan, H.O., and Aydin, N. (2021). Multi-model CNN fusion for sperm morphology analysis. Comput. Biol. Med., 137.","DOI":"10.1016\/j.compbiomed.2021.104790"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.mefs.2017.12.002","article-title":"Relationship between sperm progressive motility and DNA integrity in fertile and infertile men","volume":"23","author":"Elbashir","year":"2018","journal-title":"Middle East Fertil. Soc. J."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1900712","DOI":"10.1002\/advs.201900712","article-title":"Prediction of DNA integrity from morphological parameters using a single-sperm DNA fragmentation index assay","volume":"6","author":"Wang","year":"2019","journal-title":"Adv. Sci."},{"key":"ref_70","first-page":"349","article-title":"An optimized artificial neural network approach based on sperm whale optimization algorithm for predicting fertility quality","volume":"27","author":"Engy","year":"2018","journal-title":"Stud. Inform. Control"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"167","DOI":"10.32725\/jab.2019.015","article-title":"Prediction of semen quality using artificial neural network","volume":"17","author":"Badura","year":"2019","journal-title":"J. Appl. Biomed."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1080\/19396368.2016.1185654","article-title":"Validation of artificial neural network models for predicting biochemical markers associated with male infertility","volume":"62","author":"Vickram","year":"2016","journal-title":"Syst. Biol. Reprod. Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.cmpb.2015.08.013","article-title":"An efficient method for automatic morphological abnormality detection from human sperm images","volume":"122","author":"Ghasemian","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.compbiomed.2017.10.009","article-title":"A dictionary learning approach for human sperm heads classification","volume":"91","author":"Shaker","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s11517-019-02101-y","article-title":"A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods","volume":"58","author":"Ilhan","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.bbe.2021.12.010","article-title":"SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis","volume":"42","author":"Chen","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Haugen, T.B., Hicks, S.A., Andersen, J.M., Witczak, O., Hammer, H.L., Borgli, R., Halvorsen, P., and Riegler, M. (2019, January 18\u201321). Visem: A multimodal video dataset of human spermatozoa. Proceedings of the 10th ACM Multimedia Systems Conference, Amherst, MA, USA.","DOI":"10.1145\/3304109.3325814"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/6\/132\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:46:17Z","timestamp":1760031977000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/6\/132"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,2]]},"references-count":77,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["computation13060132"],"URL":"https:\/\/doi.org\/10.3390\/computation13060132","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,2]]}}}