Jaruenpunyasak Jermphiphut, Maneelert Prawai, Nawae Marwan, Choksuchat Chainarong
Reprod Fertil. 2025 May 2;6(2). doi: 10.1530/RAF-25-0014. Print 2025 Apr 1.
Traditional sperm morphology assessment requires staining and high magnification (100×), rendering sperm unsuitable for further use. We aimed to determine whether an in-house artificial intelligence (AI) model could reliably assess normal sperm morphology in living sperm and compare its performance with that of computer-aided semen analysis and conventional semen analysis methods. In this experimental study, we enrolled 30 healthy male volunteers aged 18-40 years at the Songklanagarind Assisted Reproductive Centre, Songklanagarind Hospital. We developed a novel dataset of sperm morphological images captured with confocal laser scanning microscopy at low magnification and high resolution to train and validate an AI model. Semen samples were divided into three aliquots and assessed for unstained live sperm morphology using the AI model, whereas computer-aided and conventional semen analysis methods evaluated fixed sperm morphology. The performance of our in-house AI model for evaluating unstained live sperm morphology was compared with that of the other two methods. The in-house AI model showed the strongest correlation with computer-aided semen analysis (r = 0.88), followed by conventional semen analysis (r = 0.76). The correlation between computer-aided semen analysis and conventional semen analysis was weaker (r = 0.57). Both the in-house AI and conventional semen analysis methods detected normal sperm morphology at significantly higher rates than computer-aided semen analysis. The in-house AI model could enhance assisted reproductive technology outcomes by improving the selection of high-quality sperm with normal morphology. This could lead to better outcomes of intracytoplasmic sperm injections and other fertility treatments.
We evaluated a new in-house AI model for assessing the shape and size (morphology) of live sperm without staining and performed comparisons with computer-aided semen analysis and conventional semen analysis, which require sperm to be fixed and stained before analysis. This new method of assessing unstained, live sperm is significant because it facilitates viable sperm selection for use in assisted reproductive technology immediately after assessment, ultimately contributing to improved fertility outcomes. The AI model allowed sperm morphology assessments with significantly improved accuracy and reliability. By using high-resolution images and advanced microscopy, the AI model could detect subcellular features. This AI model could be an effective tool in clinical settings, because it minimizes subjectivity and improves sperm selection for assisted reproductive technologies, potentially leading to higher success rates in infertility treatments. Further research can refine the model and validate its effectiveness in diverse clinical environments.
传统的精子形态评估需要染色和高倍放大（100倍），这使得精子不适合进一步使用。我们旨在确定一个内部人工智能（AI）模型能否可靠地评估活精子的正常形态，并将其性能与计算机辅助精液分析和传统精液分析方法进行比较。在这项实验研究中，我们招募了宋卡王子医院宋卡王子辅助生殖中心30名年龄在18至40岁的健康男性志愿者。我们开发了一个新的精子形态图像数据集，这些图像是用共聚焦激光扫描显微镜在低倍放大和高分辨率下拍摄的，用于训练和验证一个AI模型。精液样本被分成三份，使用AI模型评估未染色的活精子形态，而计算机辅助和传统精液分析方法则评估固定精子的形态。将我们的内部AI模型评估未染色活精子形态的性能与其他两种方法进行比较。内部AI模型与计算机辅助精液分析的相关性最强（r = 0.88），其次是传统精液分析（r = 0.76）。计算机辅助精液分析与传统精液分析之间的相关性较弱（r = 0.57）。内部AI和传统精液分析方法检测正常精子形态的比率均显著高于计算机辅助精液分析。内部AI模型可以通过改进对形态正常的高质量精子的选择来提高辅助生殖技术的成功率。这可能会带来更好的胞浆内单精子注射和其他生育治疗结果。
我们评估了一个新的内部AI模型，用于在不染色的情况下评估活精子的形状和大小（形态），并与计算机辅助精液分析和传统精液分析进行了比较，后两种方法在分析前需要对精子进行固定和染色。这种评估未染色活精子的新方法具有重要意义，因为它有助于在评估后立即选择有活力的精子用于辅助生殖技术，最终有助于改善生育结果。AI模型能够以显著提高的准确性和可靠性进行精子形态评估。通过使用高分辨率图像和先进的显微镜技术，AI模型可以检测亚细胞特征。这种AI模型在临床环境中可能是一种有效的工具，因为它最大限度地减少了主观性，并改善了辅助生殖技术中精子的选择，有可能提高不育治疗的成功率。进一步的研究可以完善该模型，并在不同的临床环境中验证其有效性。
