O-308 Is AI the future of sperm quality assessment? A comparative study of AI-enhanced and conventional semen analysis systems

精子质量 精液 精子 精液分析 男科 精液质量 生物 妇科 医学 不育 怀孕 遗传学
作者
Rafael Lafuente,Gemma López Granollers,A. Hernando,E. Carballo,Josep M. Canals,Aïda Pujol,M Popovic,Irene Miguel-Escalada,Daniel Mataró
出处
期刊:Human Reproduction [Oxford University Press]
卷期号:39 (Supplement_1)
标识
DOI:10.1093/humrep/deae108.365
摘要

Abstract Study question How does the accuracy of an artificial intelligence (AI)-enhanced semen analysis system compare to conventional semi-automated methods when assessing semen quality? Summary answer Compared to conventional semi-automated methods for sperm quality assessment, the AI-enhanced system tends to overestimate progressive sperm motility and normal sperm morphology. What is known already Male factor infertility, specifically poor sperm quality, accounts for over 40% of all infertility cases. Accurate semen analysis is thus a critical step in guiding appropriate treatment strategies in male reproductive health. Past decades have witnessed the development of semi-automated systems for semen analysis, overcoming the limitations of traditional microscopy. While these systems offer precise data on kinetics and concentration, manual intervention remains necessary for assessing certain parameters, potentially affecting reproducibility. AI-based systems have been recently introduced into clinical practice, promising more objective and standardized analysis. Yet, the extent to which these methods surpass conventional approaches remains to be established. Study design, size, duration This is a single-centre, prospective, consecutive, paired study, conducted from February to September 2022. The study included 84 sperm samples from patients undergoing medically assisted reproduction. Participants/materials, setting, methods All sperm samples were analyzed using the AI-enhanced automated system (LENSHooke X1 Pro, Bonraybio®, Fertil Ibérica) and conventional methods: CASA semi-automatized device (ISAS, Proiser), Diff-Quik staining for manual morphology assessment, and pH test strips. We measured and compared several semen parameters, including morphology (% normal sperm), concentration (million/mL), motility (% progressive, % non-progressive, % total motility), and pH, using both approaches. Spearman and Kruskal-Wallis tests were used for statistical analyses. Main results and the role of chance We observed significant differences among several semen parameters when comparing the AI-enhanced system and conventional approaches. Mean normal sperm morphology, as assessed by the AI-enhanced automated device, was significantly higher than that evaluated by conventional microscopy (4.62% versus 3.07%, p < 0.00001). We also observed significant differences for motility and pH parameters (57.98 versus 25.66, p = 0.0001 and 7.86 versus 8.38, p = 0.0001, respectively). Correlations between variables assessed using both methods also varied. Sperm concentration showed a strong correlation between the AI-enhanced and conventional systems (Spearmann’s Rho=0.828), however a weak correlation was observed for total and progressive motility, as well as the percent of immotile sperm (69.54% versus 28.98% R2=0.541; 57.98% versus 25.66% R2=0.566, and 30.45% versus 47.12%, R2=0.541, respectively). There was no significant correlation for non-progressive motility between the two methods (6.53% versus 11.60%, R2=0.157). Limitations, reasons for caution This study exclusively compared two analysis methods among numerous AI-enhanced systems available on the market. Given the rapidly evolving landscape of AI technologies, caution is warranted when generalizing these findings to other AI models, as their performance may vary. Wider implications of the findings Observed differences between the AI-enhanced system and conventional methods require careful consideration. While fully automated semen analysis systems are designed to reduce subjectivity, extensive comparative research is crucial. A comprehensive market assessment will be essential for safeguarding patient treatment outcomes and advancing AI capabilities in semen analysis. Trial registration number not applicable
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小巧吐司完成签到,获得积分20
刚刚
樱桃完成签到,获得积分10
刚刚
义气凡阳完成签到,获得积分10
刚刚
堀川完成签到,获得积分20
1秒前
哈哈哈关注了科研通微信公众号
1秒前
bkagyin应助ljl采纳,获得10
1秒前
1秒前
糟糕的鹏飞完成签到 ,获得积分20
2秒前
上官若男应助kk采纳,获得10
2秒前
汉堡包应助=Q采纳,获得10
2秒前
健壮的马里奥完成签到,获得积分10
2秒前
3秒前
3秒前
3秒前
英俊的铭应助听闻采纳,获得10
3秒前
我的miemie完成签到,获得积分10
3秒前
糊涂的雪枫完成签到,获得积分10
5秒前
5秒前
6秒前
水何澹澹完成签到,获得积分0
6秒前
L416发布了新的文献求助10
6秒前
包子关注了科研通微信公众号
6秒前
娇娇发布了新的文献求助10
7秒前
G_Y发布了新的文献求助50
7秒前
喜悦凝冬完成签到,获得积分10
7秒前
cyq发布了新的文献求助10
8秒前
yhe314992205完成签到,获得积分10
8秒前
8秒前
8秒前
小馒头完成签到,获得积分10
9秒前
在水一方应助subyjale采纳,获得10
9秒前
科研通AI5应助小胡采纳,获得10
9秒前
lalala应助hahahaha采纳,获得10
9秒前
9秒前
喜悦凝冬发布了新的文献求助10
10秒前
白沙叶完成签到,获得积分10
11秒前
852应助魔幻的依云采纳,获得10
11秒前
WizBLue发布了新的文献求助10
12秒前
慕青应助山河表里采纳,获得10
12秒前
12秒前
高分求助中
Continuum thermodynamics and material modelling 3000
Production Logging: Theoretical and Interpretive Elements 2500
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 2000
Applications of Emerging Nanomaterials and Nanotechnology 1111
Covalent Organic Frameworks 1000
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Theory of Block Polymer Self-Assembly 750
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3481607
求助须知:如何正确求助?哪些是违规求助? 3071658
关于积分的说明 9123400
捐赠科研通 2763408
什么是DOI,文献DOI怎么找? 1516476
邀请新用户注册赠送积分活动 701579
科研通“疑难数据库(出版商)”最低求助积分说明 700426