ArticleComputational and structural biotechnology journal2024
Multidimensional morphological analysis of live sperm based on multiple-target tracking.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Artificial Intelligence in the choice of sperm for ICSI - A Literature Review.JBRA assisted reproduction · 2026Pooled it
- Progress in the research of artificial intelligence in andrology: a narrative review.Translational andrology and urology · 2026Review
- Evaluation of Human Sperm Quality In Vitro-Purification of Motile Sperm and Subsequent Assessment of Potential Apoptotic Signs Beyond DNA Fragmentation.Biomolecules · 2026Review
- Integrated subcellular localization of functional fluorescence probes and functional analysis in motile spermatozoa by an AI-enhanced algorithm.Asian journal of andrology · 2026Article
- Evaluation of vision transformers and vision foundation models for sperm morphology analysis.Frontiers in reproductive health · 2026Article
- Deep Learning Models for Multi-Part Morphological Segmentation and Evaluation of Live Unstained Human Sperm.Sensors (Basel, Switzerland) · 2025Article
- Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.Apoptosis : an international journal on programmed cell death · 2025Article
- [Application of Artificial Intelligence in Sperm Quality Analysis and Sperm Screening].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Manual semen evaluation methods are subjective and time-consuming. In this study, a deep learning algorithmic framework was designed to enable non-invasive multidimensional morphological analysis of live sperm in motion, improve current clinical sperm morphology testing methods, and significantly contribute to the advancement of assisted reproductive technologies. We improved the FairMOT tracking algorithm by incorporating the distance and angle of the same sperm head movement in adjacent frames, as well as the head target detection frame IOU value, into the cost function of the Hungarian matching algorithm. For sperm morphology, we used the BlendMask segmentation method to segment individual sperm. SegNet was used to separate the head, midpiece, and principal piece comments from each sperm. Experienced in vivo sperm physicians confirmed a morphological accuracy percentage of 90.82%. A total of 1272 samples were collected from multiple tertiary hospitals for validation of the system, which were also evaluated by physicians. The results of our system were highly consistent with those of manual microscopy. This study realized the automated detection of progressive motility and morphology of sperm simultaneously, which is crucial for selection of morphologically normal and motile sperm for intracytoplasmic sperm injection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.