Jamalirad Hossein, Jajroudi Mahdie, Khajehpour Bahareh, Sadighi Gilani Mohammad Ali, Eslami Saeid, Sabbaghian Marjan, Vakili Arki Hassan
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Pharmaceutical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Hum Reprod Open. 2024 Nov 21;2025(1):hoae070. doi: 10.1093/hropen/hoae070. eCollection 2025.
How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
A comprehensive literature search was conducted following PRISMA-ScR guidelines, covering PubMed and Scopus databases from 2013 to 15 May 2024. Relevant English-language studies were identified using Medical Subject Headings (MeSH) terms. We also used PubMed's 'similar articles' and 'cited by' features for thorough bibliographic screening to ensure comprehensive coverage of relevant literature.
PARTICIPANTS/MATERIALS SETTING METHODS: The review included studies on patients with NOA where AI-based models were used for predicting m-TESE outcomes, by incorporating clinical data, hormonal levels, histopathological evaluations, and genetic parameters. Various machine learning and deep learning techniques, including logistic regression, were employed. The Prediction Model Risk of Bias Assessment Tool (PROBAST) evaluated the bias in the studies, and their quality was assessed using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines, ensuring robust reporting standards and methodological rigor.
Out of 427 screened articles, 45 met the inclusion criteria, with most using logistic regression and machine learning to predict m-TESE outcomes. AI-based models demonstrated strong potential by integrating clinical, hormonal, and biological factors. However, limitations of the studies included small sample sizes, legal barriers, and challenges in generalizability and validation. While some studies featured larger, multicenter designs, many were constrained by sample size. Most studies had a low risk of bias in participant selection and outcome determination, and two-thirds were rated as low risk for predictor assessment, but the analysis methods varied.
The limitations of this review include the heterogeneity of the included research, potential publication bias and reliance on only two databases (PubMed and Scopus), which may limit the scope of the findings. Additionally, the absence of a meta-analysis prevents quantitative assessment of the consistency of models. Despite this, the review offers valuable insights into AI predictive models for m-TESE in NOA.
The review highlights the potential of advanced AI techniques in predicting successful sperm retrieval for NOA patients undergoing m-TESE. By integrating clinical, hormonal, histopathological, and genetic factors, AI models can enhance decision-making and improve patient outcomes, reducing the number of unsuccessful procedures. However, to further enhance the precision and reliability of AI predictions in reproductive medicine, future studies should address current limitations by incorporating larger sample sizes and conducting prospective validation trials. This continued research and development is crucial for strengthening the applicability of AI models and ensuring broader clinical adoption.
STUDY FUNDING/COMPETING INTERESTS: The authors would like to acknowledge Mashhad University of Medical Sciences, Mashhad, Iran, for financial support (Grant ID: 4020802). The authors declare no competing interests.
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人工智能（AI）模型能多准确地预测接受显微睾丸精子提取（m-TESE）手术的非梗阻性无精子症（NOA）患者的精子获取情况？
AI预测模型在预测接受m-TESE的NOA患者精子获取成功方面具有重大前景，尽管研究设计的变异性、小样本量以及缺乏验证研究等局限性限制了该领域研究的整体可推广性。
先前的研究探讨了m-TESE中精子获取成功的各种预测因素，包括临床和激素因素。然而，尚未建立一致的预测模型。
研究设计、规模、持续时间：按照PRISMA-ScR指南进行了全面的文献检索，涵盖2013年至2024年5月15日的PubMed和Scopus数据库。使用医学主题词（MeSH）术语识别相关的英文研究。我们还利用PubMed的“相似文章”和“被引用文章”功能进行全面的文献筛选，以确保全面涵盖相关文献。
参与者/材料、环境、方法：该综述纳入了关于NOA患者的研究，其中基于AI的模型通过纳入临床数据、激素水平、组织病理学评估和遗传参数来预测m-TESE结果。采用了各种机器学习和深度学习技术，包括逻辑回归。使用预测模型偏倚风险评估工具（PROBAST）评估研究中的偏倚，并根据个体预后或诊断的多变量预测模型的透明报告（TRIPOD）指南评估其质量，确保稳健的报告标准和方法严谨性。
在筛选的427篇文章中，45篇符合纳入标准，大多数使用逻辑回归和机器学习来预测m-TESE结果。基于AI的模型通过整合临床、激素和生物学因素显示出强大的潜力。然而，研究的局限性包括样本量小、法律障碍以及可推广性和验证方面的挑战。虽然一些研究采用了更大规模的多中心设计，但许多研究受到样本量的限制。大多数研究在参与者选择和结果判定方面的偏倚风险较低，三分之二在预测因素评估方面被评为低风险，但分析方法各不相同。
局限性、谨慎原因：本综述的局限性包括纳入研究的异质性、潜在的发表偏倚以及仅依赖两个数据库（PubMed和Scopus），这可能会限制研究结果的范围。此外，缺乏荟萃分析妨碍了对模型一致性的定量评估。尽管如此，该综述为NOA中m-TESE的AI预测模型提供了有价值的见解。
该综述强调了先进AI技术在预测接受m-TESE的NOA患者精子获取成功方面的潜力。通过整合临床、激素、组织病理学和遗传因素，AI模型可以改善决策并提高患者预后，减少不成功手术的数量。然而，为了进一步提高AI在生殖医学预测中的精度和可靠性，未来的研究应通过纳入更大样本量并进行前瞻性验证试验来解决当前的局限性。这种持续的研发对于加强AI模型的适用性并确保更广泛的临床应用至关重要。
研究资金/利益冲突：作者感谢伊朗马什哈德医科大学提供资金支持（资助编号：�020802）。作者声明无利益冲突。
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