AI-Powered Fertility Insights: An Automated Human Sperm Analysis via Deep Learning
Source abstract
Abstract
This paper presents a semi-autonomous AI-based platform designed for the efficient management and quantitative analysis of human spermatozoa. Addressing the limitations of manual semen analysis, this system integrates advanced image processing and analytical techniques to offer a high-throughput diagnostic solution. During operation, the proposed system autonomously performs a precise quantitative assessment of sperm concentration, accurately tracks individual sperm motility patterns, and systematically classifies morphological abnormalities. The result is a comprehensive sperm analysis report, meticulously generated according to the latest established World Health Organization (WHO) guidelines for concentration, motility, and morphology. A distinguishing feature of this system is the ability to yield reliable preliminary results even with minimally pre-processed clinical samples, thereby enhancing diagnostic objectivity, efficiency, and reliability in male reproductive health assessments.
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Research context
OpenAlex topics: Sperm and Testicular Function · Reproductive Biology and Fertility · Ovarian function and disorders
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Military Institute of Clinical Embryology and Histology, Vietnam Military Medical University, Ha Noi 10000, Vietnam
Faculty of Information Systems, University of Economics and Law, Vietnam National University, Ho Chi Minh 70000, Vietnam
References (44)
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