Non-Destructive Prediction Of Sperm Dna Fragmentation Using Deep Learning: A Comprehensive Review Of Clinical, Computational, And Federated Approaches
DOI:
https://doi.org/10.29284/k7pjn125Keywords:
Artificial intelligence, Assisted reproductive technology (ART), Clinical decision support systems, Convolutional neural networks (CNN), Deep learning, Domain generalization, Federated learning, Image segmentation, Male infertility, multi-taskAbstract
Male infertility is most common cause in reproductive failure worldwide. Still, routine clinical procedures relies largely on conventional semen analysis standardized by the World Health Organization (WHO), which is less accurate to provide details regarding molecular integrity of sperm DNA [2]. In recent years, sperm DNA fragmentation (SDF) has evolved as a measurable indicator for impaired fertilization, compromised embryo development, recurrent pregnancy loss, and less success rates in assisted reproductive technologies [18]. However, laboratory techniques for assessing SDF – such as TUNEL, SCSA and Comet assay - are destructive in nature and difficult to apply consistently through diverse clinical settings.
This review gives a detailed and clinically grounded overview for deep learning approaches for non-destructive SDF prediction using sperm microscopy images. Evolution of neural networks and distributed learning systems provide advancement in medical imaging [10]. We analyse the advancement of convolutional neural networks (CNNs) and transformer-based architectures in biomedical image analysis including AI-assisted sperm assessment.
This review dives deeper into multi-task learning strategies that jointly model related sperm characteristics, as well as attention-based multiple-instance learning techniques that enable reliable sample-level predictions from collections of individual cell images. To handle variability across laboratories and imaging platforms, we study domain generalization and adaptation methods along with self-supervised and in-silico labelling approaches that enhance data efficiency.
Federated learning model approaches are adopted for the development of AI systems in fertility clinics in a decentralized manner. This research aims to build upon the current body of knowledge in the areas of harmonization, the application of double-blind annotation approaches, the resolution of reproducibility problems associated with morphological grading, dealing with the variability of different domains, and the identification of potential biases in medical image datasets. Calibration approaches and interpretability techniques, including gradient-based saliency techniques and feature-based techniques such as Grad-CAM and SHAP, are identified as critical components in the development of trustworthy AI systems.
Previous research has shown that multi-task and federated deep learning models have better performance than traditional single-task models in terms of morphology assessment and SDF prediction, and that they have robust performance in adverse imaging conditions and across different domains.
Through the integration of clinical reproductive diagnostics with scalable and privacy-conscientious frameworks of neural learning, AI-assisted computer-assisted systems demonstrate significant promise for facilitating objective, reproducible, and clinically viable fertility assessments. The analysis presented here identifies existing unsolved bottlenecks related to annotated data availability, domain adaptation challenges, clinical validation requirements, and regulatory readiness while outlining future research directions related to the interplay of medical imaging, distributed optimization, and trustworthy AI. Together, these developments highlights the increasing potential of non-destructive SDF prediction as a transformative application of next-generation learning systems in precision reproductive medicine.Top of Form
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
