ReviewBMC urology2025
Deep learning-based approach for sperm morphology analysis.
Review in BMC urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Bioinformatics and multi-omics approaches in male infertility: implications for diagnosis and assisted reproduction.Journal of assisted reproduction and genetics · 2026Review
- A Multi-Teacher Knowledge Distillation Framework for Enhancing the Robustness of Automated Sperm Morphology Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluation of vision transformers and vision foundation models for sperm morphology analysis.Frontiers in reproductive health · 2026Article
- Performance comparison of YOLO, Faster R-CNN, and HRNet architectures for bull sperm viability assessment.Frontiers in veterinary science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Male infertility is a highly prevalent condition throughout the world. Sperm morphology analysis（SMA） is one of most important examination for evaluating male infertility. This paper highlights the strengths, limitations, and clinical applicability of conventional machine learning (ML) models and deep learning (DL) models in SMA from various studies. Simultaneously, we explore the potential role of segmentation and classification of complete sperm structure based on deep learning algorithms. Therefore, this narrative literature review aims to summarize the current evidence of artificial intelligence and machine learning applications for sperm morphology analysis and explore further recommendations about deep learning algorithms applications to practically enhance the performance.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.