[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82931-en":3,"doc-seo-82931-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82931,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation","Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation is labor-intensive, operator-dependent, and limited by inter- and intra-observer variability, motivating objective, reproducible computational methods. This review synthesizes AI-driven sperm analysis, emphasizing computer vision, deep learning, multimodal fusion, robustness, and clinical translation. It covers detection, motility tracking, segmentation, morphology/defect classification, functional and genetic integrity assessment, plus datasets, benchmarks, metrics, deployment barriers, and a staged clinical translation roadmap.","Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation  \nRunwei Guanc,d,1 , Shaofeng Liangc,1 , Jiacheng Wenge,1 , Xiaoyi Gud, Jia Wengf , Daizong Liuj , Duo Pani , Qingxin Zhangh , Xiao Liangb , Weiping Dingg , Suoyu Zhub,∗∗ , Ming Yuana,∗ and Yanhua Feia,∗∗∗  \na Department of Gynaecology and Obstetrics, The Affiliated Jiangyin Hospital of Nantong University, Jiangyin, China  \nb Department of Oncology, the Affiliated Jiangyin Hospital of Nantong University, Jiangyin, China c Thrust of AI, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China d FertiTech AI, Shanghai, China  \ne Department of Oncology, Suzhou Xiangcheng People’s Hospital, Suzhou, China  \nf Department of Biological Sciences and Bioinformatics, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou, China g School of Artificial Intelligence and Computer Science, Nantong University, Nantong, China  \nh Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, USA  \ni Sycamore Research Institute of Life Sciences, Shanghai, China j Institute for Math & AI, Wuhan University, Wuhan, China  \nARTICLE INFO  \nKeywords:  \nSperm analysis Computer vision Deep learning Multimodal fusion Male infertility  \nAssisted reproductive technology Trustworthy AI  \nAB STRACT  \nMale infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by interand intra-observer variability, motivating the development of objective and reproducible computational approaches. This review provides a comprehensive and perspective-oriented synthesis of artificial intelligence-driven sperm analysis, with a focus on computer vision, deep learning, multimodal fusion, robustness, and clinical translation. We first review task-specific methods for sperm detection and counting, tracking-based motility assessment, semantic and instance segmentation, morphology and defect classification, functional assessment, and genetic integrity evaluation. We then summarize public datasets, benchmarks, evaluation metrics, and emerging multimodal strategies that integrate microscopic images, time-lapse videos, CASA-derived parameters, DNA integrity assays, and clinical metadata. Beyond algorithmic performance, we discuss key barriers to real-world deployment, including data scarcity, cross-center domain shift, annotation inconsistency, interpretability, uncertainty calibration, privacy-preserving learning, and workflow integration. Finally, we outline a staged clinical translation roadmap spanning technical standardization, multicenter retrospective validation, silent prospective evaluation, human-in-the-loop clinical testing, ART outcome validation, regulatory approval, and postmarket monitoring. By organizing the field from task-specific visual recognition to trustworthy multimodal reproductive intelligence, this review highlights both the progress and the unresolved challenges required to translate AI-driven sperm analysis into clinically meaningful decision support.  \n1. Introduction  \nMale infertility remains a significant global health concern, contributing to approximately 40–50% of all infertility cases among couples [86] . Accurate and timely diagnosis is paramount for effective management and treatment, particularly in the context of assisted reproductive technologies (ART) such as in-vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) . The cornerstone of male fertility assessment has traditionally been manual semen analysis, a procedure that evaluates key parameters including sperm concentration, motility, and morphology  \narXiv :2607 .053 1 1v 1 [ cs .CV] 6 Jul 2026  \n∗Corresponding author  \n [runwayrwguan@hkust-gz.edu.cn](runwayrwguan@hkust-gz.edu.cn) ","cbCaisUX9GCOBtyc","https://ap.wps.com/l/cbCaisUX9GCOBtyc","pdf",4107704,1,46,"English","en",105,"# Introduction\n# Computer-Vision and Deep-Learning Tasks\n## Detection and Counting\n## Motility Assessment and Tracking\n## Segmentation and Morphology/Defect Classification\n## Functional and Genetic Integrity Evaluation\n# Datasets, Benchmarks, and Metrics\n# Multimodal Strategies and Integration\n# Barriers to Real-World Deployment\n# Clinical Translation Roadmap","[{\"question\":\"Why is AI-driven sperm analysis needed in male infertility?\",\"answer\":\"Conventional semen evaluation is labor-intensive and operator-dependent, with inter- and intra-observer variability. AI methods aim to provide more objective and reproducible assessment for diagnosis and ART planning.\"},{\"question\":\"What AI tasks does the review cover for sperm analysis?\",\"answer\":\"The review covers sperm detection and counting, tracking-based motility assessment, semantic and instance segmentation, morphology and defect classification, functional assessment, and genetic integrity evaluation.\"},{\"question\":\"How does the review approach clinical translation of these models?\",\"answer\":\"It proposes a staged roadmap including technical standardization, multicenter retrospective validation, silent prospective evaluation, human-in-the-loop clinical testing, ART outcome validation, regulatory approval, and postmarket monitoring.\"}]",1784184072,116,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"deep-learning-for-semen-analysis-in-male-infertility-computer-vision-multimodal-fusion-and-clinical-translation","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/deep-learning-for-semen-analysis-in-male-infertility-computer-vision-multimodal-fusion-and-clinical-translation/82931/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is AI-driven sperm analysis needed in male infertility?","Question",{"text":75,"@type":76},"Conventional semen evaluation is labor-intensive and operator-dependent, with inter- and intra-observer variability. AI methods aim to provide more objective and reproducible assessment for diagnosis and ART planning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What AI tasks does the review cover for sperm analysis?",{"text":80,"@type":76},"The review covers sperm detection and counting, tracking-based motility assessment, semantic and instance segmentation, morphology and defect classification, functional assessment, and genetic integrity evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the review approach clinical translation of these models?",{"text":84,"@type":76},"It proposes a staged roadmap including technical standardization, multicenter retrospective validation, silent prospective evaluation, human-in-the-loop clinical testing, ART outcome validation, regulatory approval, and postmarket monitoring.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]