Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm

SIMPLE SUMMARY: The acrosome reaction (AR) is one of the important factors in assessing sperm infertility. However, the accuracy of these assessments may be influenced by the subjective judgments of experts. Addressing the issue of subjectivity in the assessment of the AR, we developed the Acrosome Reaction Classification System (ARCS). This system enables automatic calculation of the AR ratio using deep learning, which not only detects AR sperm by identifying micro-changes in the plasma membrane (PM), but also offers improved speed and performance compared to experts. Moreover, we established the need for independent ARCS with appropriate magnifications to detect AR sperm across various magnifications. The ARCS also offers consistent analysis for AR sperm detection and reduces misrecognition due to human error. In conclusion, our proposed methodology has the potential to contribute to the development of deep learning-based diagnostic models for sperm characteristics in pigs and other species, while the ARCS can be utilized in artificial intelligence-based infertility diagnoses within reproductive medicine. ABSTRACT: The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception–ResNet v2 architectures. These models distinctly recognized micro-changes in the PM of AR sperms. Moreover, the Inception–ResNet v2-based ARCS achieved a mean average precision of over 97%. Our system’s calculation of the AR ratio on the test dataset produced results similar to the work of the three experts and could do so more quickly. Our model streamlines sperm detection and AR status determination using a CNN-based approach, replacing laborious tasks and expert assessments. The ARCS offers consistent AR sperm detection, reduced human error, and decreased working time. In conclusion, our study suggests the feasibility and benefits of using a sperm diagnosis artificial intelligence assistance system in routine practice scenarios.