ArticleDiagnostics (Basel, Switzerland)2020
Deep Learning-Based Morphological Classification of Human Sperm Heads.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.Journal of medical systems · 2023Pooled it
- Intelligent Staging Performance of Diabetic Retinopathy Based on Fundus Fluorescein Angiography Images with Different Angiographic Phases.Bioengineering (Basel, Switzerland) · 2026Article
- A two-stage sperm holomorphological analysis method based on multi-output network construction.BMC bioinformatics · 2026Article
- A Multi-Teacher Knowledge Distillation Framework for Enhancing the Robustness of Automated Sperm Morphology Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- Adaptive Logit Fusion for Mitigating Class Imbalance in Multi-Category Sperm Morphology Assessment.Life (Basel, Switzerland) · 2026Article
- GPTNeXt: Biomedical Image Classification Investigations.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-enabled morphology analysis of bovine sperm for label-free imaging flow cytometry.Frontiers in veterinary science · 2026Article
- Deep-learning based model for sperm morphology assessment using the SMD/MSS dataset.Future science OA · 2025Article
- Automated AI for real-time sperm selection in ICSI: reducing variability and studying the role of sperm in embryo development.Reproductive biology and endocrinology : RB&E · 2025Observational
- Deep learning-based approach for sperm morphology analysis.BMC urology · 2025Review
- Health-related quality of life and sexual function among women with overweight or obesity and urinary incontinence: a cross-sectional study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Category-Aware Two-Stage Divide-and-Ensemble Framework for Sperm Morphology Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence model for the assessment of unstained live sperm morphology.Reproduction & fertility · 2025Article
- MRI assessment of sacral injury location and analysis of influencing factors after high-intensity focused ultrasound ablation for patients with uterine fibroids.Frontiers in physiology · 2025Article
- Automatic identification of human spermatozoa with zona pellucida-binding capability using deep learning.Human reproduction open · 2025Article
- Mixed chronic scrotal pain secondary to piriformis scarring treated with PRF: case report.Frontiers in medicine · 2025Article
- Nanoplastics and fungi: exploring dual roles in degradation and pathogenicity.Frontiers in microbiology · 2025Review
- Feasibility of artificial intelligence-assisted fast magnetic resonance imaging technology in the ankle joint injury: a comparison of the proton density-weighted image.Frontiers in radiology · 2025Article
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3 authors at 1 institution in 1 country.
Funding
Abstract
Human infertility is considered as a serious disease of the reproductive system that affects more than 10% of couples across the globe and over 30% of the reported cases are related to men. The crucial step in the assessment of male infertility and subfertility is semen analysis that strongly depends on the sperm head morphology, i.e., the shape and size of the head of a spermatozoon. However, in medical diagnosis, the morphology of the sperm head is determined manually, and heavily depends on the expertise of the clinician. Moreover, this assessment as well as the morphological classification of human sperm heads are laborious and non-repeatable, and there is also a high degree of inter and intra-laboratory variability in the results. In order to overcome these problems, we propose a specialized convolutional neural network (CNN) architecture to accurately classify human sperm heads based on sperm images. It is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficiency and effectiveness. It is demonstrated that our proposed architecture outperforms state-of-the-art methods, exhibiting 88% recall on the SCIAN dataset in the total agreement setting and 95% recall on the HuSHeM dataset for the classification of human sperm heads. Our proposed method shows the potential of deep learning to surpass embryologists in terms of reliability, throughput, and accuracy.
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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.